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Enregistrement W4394802465 · doi:10.1097/01.ju.0001008660.87408.90.06

MP01-06 miRNA AS A LIQUID BIOMARKER TO DETECT MALIGNANCY IN SMALL TESTICULAR MASSES

2024· article· en· W4394802465 sur OpenAlexaboutno aff
Julián Chavarriaga, Carley Langleben, João Lobo, Lucia Nappi, George M. Yousef, Sajjad Janfaza, Nuno Tiago Tavares, Qiang Ding, Adam Bobrowski, Susan Prendeville, Lynn Anson‐Cartwright, Cármen Jerónimo, Heidi Wagner, Keith Jarvi, Martin O’Malley, Ricardo Leão, Katherine Lajkopsz, Robert J. Hamilton

Notice bibliographique

RevueThe Journal of Urology · 2024
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMicroRNA in disease regulation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiomarkermicroRNAMalignancyMedicineCancer researchPathologyBiologyGeneGenetics

Résumé

récupéré en direct d'OpenAlex

You have accessJournal of UrologyPenile & Testicular Cancer I (MP01)1 May 2024MP01-06 miRNA AS A LIQUID BIOMARKER TO DETECT MALIGNANCY IN SMALL TESTICULAR MASSES Julian Chavarriaga, Carley Langleben, João Lobo, Lucia Nappi, George M. Yousef, Sajjad Janfaza, Nuno Tiago Tavares, Qiang Ding, Adam Bobrowski, Susan Prendeville, Lynn Anson-Cartwright, Carmen Jeronimo, Heidi Wagner, Keith Jarvi, Martin O'Malley, Ricardo Leão, Katherine Lajkopsz, and Robert J. Hamilton Julian ChavarriagaJulian Chavarriaga , Carley LanglebenCarley Langleben , João LoboJoão Lobo , Lucia NappiLucia Nappi , George M. YousefGeorge M. Yousef , Sajjad JanfazaSajjad Janfaza , Nuno Tiago TavaresNuno Tiago Tavares , Qiang DingQiang Ding , Adam BobrowskiAdam Bobrowski , Susan PrendevilleSusan Prendeville , Lynn Anson-CartwrightLynn Anson-Cartwright , Carmen JeronimoCarmen Jeronimo , Heidi WagnerHeidi Wagner , Keith JarviKeith Jarvi , Martin O'MalleyMartin O'Malley , Ricardo LeãoRicardo Leão , Katherine LajkopszKatherine Lajkopsz , and Robert J. HamiltonRobert J. Hamilton View All Author Informationhttps://doi.org/10.1097/01.JU.0001008660.87408.90.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 1-4% of individuals undergoing scrotal ultrasounds are found with incidental small (≤ 2cm) testicular mases (STMs), with the vast majority being benign (∼13-21% malignant). Distinguishing between malignant and benign STMs remains a challenge, as neither serum tumor markers nor imaging methods offer reliable predictive capabilities. This study explores the potential of miRNAs, as liquid biomarkers for predicting germ cell tumours (GCTs) in STMs. METHODS: Pre-orchiectomy serum/plasma samples, drawn between 1 day and<6 months before surgery, were analyzed using different miRNA extraction methods (qRT-PCR, DdPCR) and platforms across three research laboratory facilities in three different centres (Portugal, Vancouver, Toronto). The primary endpoint of our study was the association between miRNA (miR-371a-3p) and the presence of GCTs. Additionally, we analyzed miRNAs 372, 373 and 367 aiming to improve the diagnostic performance of miRNA to predict GCTs in STMs. Our research laboratories used quantitative (Ct value <40) and qualitative analysis. We used the area under the receiver operating characteristic curve (AUROC) calculations to establish optimal thresholds for miRNAs. Comparison of pre-orchiectomy miRNA and surgical pathology was done using the Wilcoxon rank-sum test. RESULTS: From 2009 to 2023 we identified 65 patients with STMs who had banked serum/plasma prior to orchiectomy. Our cohort included 41 patients with confirmed GCTs, 20 with benign histology, and four patients who had been on surveillance for >12 months and were deemed to have benign STMs. The median age was 38 years, median tumour size was 13.5 mm (9-19), 77% and 12% underwent radical and partial orchiectomy, respectively. Of the patients with GCTs 27 (67.5%) had seminoma and 13 (32.5%) Nonseminomatous GCTs. Of the benign tumours 31% were sex cord-stromal (6 Leydig and 2 Sertoli cell tumours). Our first lab used magnetic beads-based for extraction on serum. miR371a-3p showed a sensitivity, specificity, PPV and NPV of 67.5%, 100%. 100%, and 62.5%, respectively. The AUROC was 0.774. We examined plasma with a qRT-PCR extraction kit in our second research laboratory. With a Ct mean threshold of >28, miR371a-3p showed a sensitivity and specificity of 92.3% and 85%, the AUROC was 0.912 (95% CI 0.826-0.998; p<0.0001). Other miRNAs were not informative. CONCLUSIONS: This is the largest series of STMs with banked blood/serum to date, our unique interlaboratory comparison represents a meaningful contribution to the field. miR-371a-3p appears sensitive to detect the presence of GCTs in STMs. Further research in this area is needed and could revolutionize the approach to managing these incidental STMs. Source of Funding: Agnico-Eagle Grand Challenge © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e3 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Julian Chavarriaga More articles by this author Carley Langleben More articles by this author João Lobo More articles by this author Lucia Nappi More articles by this author George M. Yousef More articles by this author Sajjad Janfaza More articles by this author Nuno Tiago Tavares More articles by this author Qiang Ding More articles by this author Adam Bobrowski More articles by this author Susan Prendeville More articles by this author Lynn Anson-Cartwright More articles by this author Carmen Jeronimo More articles by this author Heidi Wagner More articles by this author Keith Jarvi More articles by this author Martin O'Malley More articles by this author Ricardo Leão More articles by this author Katherine Lajkopsz More articles by this author Robert J. Hamilton More articles by this author Expand All Advertisement PDF downloadLoading ...

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,069

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0210,011

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,261
Écart entre enseignants0,249 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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