Abstract B043: A core inflammatory gene network associated with poor prognosis serves chemokine production in cancer associated fibroblasts in pancreatic ductal adenocarcinoma
Notice bibliographique
Résumé
Abstract Introduction: Inflammation plays an important role on the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC). Nevertheless, due to the variable inflammatory characteristics and TME profiles among PDAC patients, it is still unclear which inflammatory factors are crucially associated with PDAC prognosis and how the TME is influenced. Previously, we found a core inflammatory gene network (CIGN) by analyzing bulk RNA seq data of 183 PDAC patients from the Cancer Genome Altas (TCGA) based on 104 inflammatory gene sets from the Molecular Signatures Database. The CIGN is defined by two markers (DCBLD2 and PLAU) and is associated with poor prognosis. Single-cell RNA (scRNA) seq data provides valuable information on multiple types of cells, and it is necessary to utilise scRNA data to analyze the impact of our CIGN on PDAC TME. Method: To investigate the tumor microenvironment (TME) associated with CIGN, we employed both bulk and scRNA seq data of tumor tissue from PDAC patients from TCGA and Gene Expression Omnibus. Firstly, the Enrichplot package and online Metascape were used to perform functional enrichment analysis of genes increased in association with CIGN from bulk RNA seq data. Secondly, CIGN identified prognostic criteria from bulk RNA seq was applied to scRNA data. Then, the Seurat package was used to analyse three scRNA seq series: GSE212966 (6 PDAC patients), GSE155698 (15 PDAC patients), and GSE214295 (3 PDAC patients). Finally, the effects of CIGN on cancer-associated fibroblasts (CAFs) (proliferation, migration and chemokine expression profile)were investigated ex vivo in mouse CAFs isolated from KPC mice. Results: Genes associated with CIGN were enriched in processes related to the extracellular matrix, endoderm formation, collagen binding, response to wounding and receptor-ligand activity. Then, we performed CIGN grouping on three scRNA seq series and found a higher accumulation of CAFs in CIGN group compared to non-CIGN group, while pancreatic progenitor cell infiltration in CIGN group was much lower, implying a more immune suppressive, desmoplastic, and hypoxic TME in CIGN patients. Furthermore, we found two marker genes of CIGN (DCBLD2 and PLAU) in both expressed mostly in fibroblast cells. Moreover, in CAFs, DCBLD2 and PLAU expression is significantly higher in the CIGN group than in the non-CIGN group. Finally, the proliferation and migration rates of si-DCBLD2 and si-PLAU groups were significantly decreased compared with the control group ex vivo. What is more, the secretion of inflammatory chemokine (CXCL7 and CXCL12) increased in supernatants of CAFs. Conclusion: A core inflammatory gene network was found specifically functions in CAF and induced chemokine production in pancreatic ductal adenocarcinoma. Fundings: Hong Kong Theme-based Scheme (T12-201/20-R) Citation Format: Fangfei Li, Liu Yang, Zheng Chen, Shuangying Qiao, Yalan Sheng, Debajyoti Chowdhury, Hiu Fung Yip, Meiheng Sun, Aiping Lu. A core inflammatory gene network associated with poor prognosis serves chemokine production in cancer associated fibroblasts in pancreatic ductal adenocarcinoma [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 B043.
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 | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».