Abstract A027: Exploring autoantibody response to proteins encoded by prostate cancer driver genes
Notice bibliographique
Résumé
Abstract Prostate cancer (PCa) is a complex disease driven by genomic alterations, and understanding the immune response to these alterations can provide valuable insights into the pathogenesis. This study aimed to explore the immunogenicity of proteins encoded by PCa driver genes and to further identify corresponding autoantibodies as potential biomarkers. Through a comprehensive screening process involving bioinformatics analysis based on published studies, a set of PCa driver genes was identified for further investigation. Three strategies were employed to identify candidate driver genes: (1) utilizing the IntOGen database (Martínez-Jiménez et.al, Nature Reviews Cancer 2020), nine cohorts comprising 1503 primary PCa patient samples were analyzed, resulting in the identification of 63 PCa driver genes, with the top 20 genes exhibiting high mutation rates and frequencies in these cohorts; (2) employing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, 16 driver genes that were enriched in prostate cancer pathways were identified from the initial 63 genes; (3) referring to a published article (Dietlein et.al, Nature Genetics 2020), the top 17 driver genes specific to prostate cancer across 28 tumor types were selected. A final set of 15 PCa driver genes, which appeared in at least two of the screening strategies, were included in subsequent analyses. To assess the presence of autoantibodies targeting proteins encoded by these 15 PCa driver genes, an enzyme-linked immunosorbent assay (ELISA) was performed. Autoantibodies to proteins encoded by the selected PCa driver genes were evaluated using serum samples from PCa patients and normal controls. The results revealed that seven out of the 15 autoantibodies showed significantly elevated levels in the sera of PCa patients compared to normal controls. Notably, the autoantibody targeting SPOP (speckle-type POZ protein) exhibited the highest frequency of 42.4%, followed by the autoantibody to PTEN (phosphatase and tensin homolog) at 37.4% in the PCa group. These findings suggest that proteins encoded by PCa driver genes may possess immunogenic properties, leading to the production of corresponding autoantibodies. The identification of specific autoantibodies associated with PCa driver genes could offer novel opportunities for the discovery of autoantibody-based biomarkers in prostate cancer. Further validation and characterization of these autoantibodies are warranted to evaluate their diagnostic or prognostic potential and elucidate their functional roles in PCa pathogenesis. Citation Format: Jian-Ying Zhang, Cuipeng Qiu, Xiao Wang, Bofei Wang. Exploring autoantibody response to proteins encoded by prostate cancer driver genes [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A027.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».