Abstract PO-021: TCGA differentially expressed genes between Caucasians and African Americans at variable analytic stringencies
Notice bibliographique
Résumé
Abstract Introduction: In interrogating large dimensional datasets, many methods have evolved to correctly identify differentially expressed genes at acceptable false discovery rates. We examined the NCI TCGA head and neck gene set with open source programs (at varying stringencie)s between Caucasian and African-Americans (AA) specimens and catalogued differentially expressed genes. Procedures: cBioPortal and UALCAN were utilized to investigate the Tumor Cancer Genome Atlas (TCGA). We compared AA and Caucasian samples for this analysis. Tumor samples for both groups were controlled for tumor stage (T3 and T4), HPV status (-), and age (40-75). Three different methods were utilized to identify potential genes at acceptable high stringency (defined as probability <2 false positives out of 20,000 genes being tested). First, those genes differentially expressed in AA vs Cau populations, as well as, those significantly differentially expressed in lymph node-positive (N+) vs negative (N0) patients were investigated (p<0.01 in both groups). Secondly, Benjamin-Hochberg (BH) analysis analyzed differentially expressed genes between just AA vs Cau groups (q<0.05). Finally, genes between these two groups possessing a p-value <0.0001, but not included in the BH analysis, were included in a third analysis. Results: Benjamin-Hochberg analysis identified four differentially expressed genes (q<0.05). There were 15 additional genes in this groups with p-values <0.0001. Analysis of genes differentially expressed in both AA N+ vs N0, as well as, those differentially expressed in AA vs Cau (p<0.01) identified one additional gene (PPIL2). Expression of this gene was found to be increased in a step-wise fashion with increased tumor grade (p<0.05). Three of the four genes identified by BH analysis were differentially expressed in HNSCC tumors vs control (LIN52, CTNNA2, PRMT6). Increased expression of LIN52 and PRMT6 were grade dependent (p<0.05). In the third analysis (AA vs Cau differentially expressed genes with p<0.0001), seven of 15 genes were overexpressed in tumor vs normal tissue; five of these were associated with increased tumor grade (p<0.05). All genes associated with increased tumor grade were demonstrated increased expression in AA groups vs Cau. Conclusions: We conclude open source TCGA programs could be readily employed to identify differentially expressed genes between both groups at varying stringencies. The high-stringency Benjamin-Hochberg analytical tool identified 4 genes differentially expressed between AA vs Cau T3/T4 HPV- tumors. Genes differentially expressed in both AA N+ vs N0, as well as, AA vs Cau revealed one potential gene of interest. When applying a p-value of < 0.0001, 15 additional genes were identified. The low overall number of AA specimens in this dataset was limiting and future biobanks should favor enriched collection of all stages of head and neck cancer in under represented groups. Citation Format: Seth Buryska, Jacob Tupa, Frank Ondrey. TCGA differentially expressed genes between Caucasians and African Americans at variable analytic stringencies [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-021.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,005 | 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 ».