ArticleTranslational cancer research2026
Mature tertiary lymphoid structures tumor microenvironment-based risk model to assess patients with pancreatic ductal adenocarcinoma.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Tertiary lymphoid structures (TLS) are associated with favorable prognosis and immunotherapy response in various cancers. However, a definitive, TLS-derived gene signature for predicting outcomes in pancreatic ductal adenocarcinoma (PDAC) is lacking. This study aimed to develop and validate a robust TLS-based prognostic model for PDAC. Methods: We evaluated TLS presence in PDAC samples from a The Cancer Genome Atlas (TCGA) cohort using an 11-chemokine gene signature, stratifying patients into TLS-high and TLS-low groups. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) networking were employed to identify TLS-related hub genes. A prognostic risk model was constructed via Cox regression analysis. The model's association with the tumor immune microenvironment (TIME) and immunotherapy response was further explored using computational algorithms (CIBERSORT, ESTIMATE) and validated in the IMvigor210 cohort. Results: TLS-high PDAC tumors exhibited an inflamed immune phenotype with enhanced immune cell infiltration. We identified a gene module and key hub genes included Conclusions: We successfully developed a novel TLS-derived gene signature that robustly predicts patient survival and immunotherapy efficacy in PDAC. This model serves as a valuable prognostic biomarker and provides insights into the immune mechanisms of PDAC, supporting the strategy of inducing TLS formation to augment cancer immunotherapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.