Identifying Genes that Could Determine Prognostication in Sinonasal Squamous Cell Carcinoma
Notice bibliographique
Résumé
Background: Sinonasal squamous cell carcinoma (SNSCC) is a multifaceted pathology, with several different genetic components and known etiologies. These differences create variable prognoses and pose a unique challenge to skull base surgeons. SNSCC has been studied in several instances, but most literature analyzes specific genes in isolation, which may leave key targets for therapy unidentified. RNA and DNA sequencing can help with identifying prognostic factors as well as in the development of molecular targets in the management of sinonasal squamous cell carcinoma. While individual genes and their role in prognosis have been studied, the effect that a group of genes have on prognosis is unclear. Objective: This article aims to assess the relationship between genetic mutations and overall survival in SNSCC. Methods: Nineteen SNSCC samples were analyzed using the Tempus xT panel, a third-party DNA and RNA sequencing service, with accompanying chart review for demographic and survival data. This panel detects single nucleotide variants, indels, and copy number variants in 648 genes and chromosomal rearrangements in a subset of 21 genes. A log-rank test was performed for each gene type to compare overall survival. Logistic regression was also performed to analyze the association between mutation type and demographic characteristics. Results: Seventy-nine percent of research subjects were male with a mean age of 67 years (range 48–92). At the time of diagnosis, 84% of participants were stage T4, and 3 tumors were associated with inverted papillomas. Among patients with documented mortality, mean survival was 23 months, while those without documented mortality had a mean follow-up of 42 months. The most frequent mutations were P53 (74%), KMT2D (42%), CDKN2A (26%), CDKN2B (21%), EGFR (21%), FAT1 (21%), and MTAP (21%). Gain of function mutations in CUL1 and EZH2 were found to be associated with higher risk of mortality ( p = 0.0247). Increased mortality rates were associated with deletion of several genes including ING5, BRAF pseudogene, PRDM11, MAP2K, PSMD2, ELMOD1, LGMN, RMND5B, PNLIPRP1, RAI1, RP11, and TMEM66 (p = 0.0359). Deletions of APOA1, CYP2D6, EMC3, FCRL3, HLA-DBQ1, HOXA11, RBPMS, SEC24A, and TMC4 were also found to be associated with higher mortality ( p < 0.001). No specific gene mutations were associated with mortality within 12 months of diagnosis, T4 status at diagnosis, or poorly differentiated tumors. Similarly, overall tumor mutation burden did not correlate with these outcomes. Conclusion: This study provides a comprehensive genomic analysis of SNSCC and identifies new targets associated with increased mortality risks. Larger studies are required to confirm these findings and help with possible molecular treatment strategies. Publication History Article published online: 07 February 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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 | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 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 ».