Abstract PO-038: Metabolic-inflammatory investigation of head and neck cancer patient reported neuropsychological factors via multiomics integration of the plasma metabolome, lipidome, and circulating inflammation cytokines
Notice bibliographique
Résumé
Abstract Background: Neuropsychological factors (NPF) (i.e., fatigue, pain, cognitive dysfunction, depression, stress, and sleep disturbance) are common in head and neck cancer (HNC) and often cluster together. They can impair a patient’s functional status and quality of life and are an obstacle to effective treatment and a full recovery. Metabolism and inflammation may be underlying mechanisms but may work differently across HPV-related and unrelated HNC. We explored the joint interaction of metabolism, inflammation, and NPF via a stratified multiomics network analysis of the pretreatment plasma metabolome, lipidome, and inflammation cytokines across HPV-related and unrelated HNC. Methods: HNC patients completed validated symptom questionnaires (e.g., Multidimensional Fatigue Inventory, Patient Health Questionnaire-8, Perceived Stress Scale, Pittsburg Sleep Quality Index) and donated blood for untargeted (metabolome) and targeted (lipids and cytokines) assays prior to chemoradiotherapy. Metabolites and lipids were measured by liquid-chromatography high resolution mass spectrometry and the cytokines were measured via multiplex assays. A multiomics network analysis algorithm (xMWAS) plotted network graphs for HPV-related and unrelated HNC separately, by estimating pair-wise partial least squares correlations between the metabolites, lipids, cytokines, and NPF variables. To interpret each network, a multilevel community detection algorithm identified highly correlated clusters of variables, representing possible biological relatedness. Metabolic pathway analysis provided functional interpretation of the metabolite-lipid-cytokine-NPF clusters. Results: There were 82 subjects (59 years mean age, 72% male, 81% white, 48% HPV-related) in which, 186 metabolites, 78 lipids, 7 cytokines (C-reactive protein, Tumor necrosis factor-α, interleukin-1β, IL6, IL10, IL1 receptor antagonist, and TNF receptor-1) and 6 NPF were analyzed. We required a minimum correlation of 0.3 and P-value <0.05 to be included in the network. xMWAS modeled 525 correlations in HPV-related HNC compared to 419 correlations in HPV-unrelated. Three clusters were identified for HPV-related HNC: all 6 NPF were contained in a single cluster with 51 metabolites, 10 lipids, but 0 cytokines. Pathway analysis found enriched levels of aminoacyl-tRNA biosynthesis (P<.001), valine, leucine, and isoleucine biosynthesis (P<.001), and glycine, serine and threonine metabolism (P=0.003). Comparatively, five clusters were identified for HPV-unrelated HNC: the 6 NPF were dispersed between two closely linked clusters along with 23 metabolites, 4 lipids, and 4 cytokines (CRP, IL1β, IL10, IL1ra). Enriched pathways included aminoacyl-tRNA biosynthesis (P<.0001), glycine, serine, and threonine metabolism (P<.0001), cysteine and methionine metabolism (P<.0001). Conclusions: In both HPV-related and unrelated HNC, NPF were closely linked via metabolites enriched in amino acid metabolic pathways, suggesting that NPF may have an amino acid metabolic foundation. Cytokines may play a larger role in HPV unrelated HNC. Citation Format: Ronald C. Eldridge, Yufen Lin, Nabil F. Saba, Andrew Miller, Evanthia C. Wommack, Jennifer Felger, Deborah W. Bruner, Canhua Xiao. Metabolic-inflammatory investigation of head and neck cancer patient reported neuropsychological factors via multiomics integration of the plasma metabolome, lipidome, and circulating inflammation cytokines [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-038.
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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».