Abstract B043: A core inflammatory gene network associated with poor prognosis serves chemokine production in cancer associated fibroblasts in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".