ArticleScientific reports2025
Characterization of pancreatic cancer stem cells and construction of a stem cell-based prognostic signature.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Pancreatic cancer is a gastrointestinal malignancy with a dismal prognosis. Cancer stem cells (CSCs) are considered key drivers of its aggressiveness, metastatic capacity, and resistance to treatment. However, the intratumoral heterogeneity of CSCs and their roles within the tumor microenvironment remain poorly characterized. We integrated single-cell transcriptomic data (GSE214295) with large-scale bulk RNA-seq datasets (TCGA-PAAD, GEO and CPTAC) for comprehensive analysis. Malignant epithelial subpopulations were identified and their stemness evaluated using Seurat, CopyKAT, and CytoTRACE. Monocle2, scMetabolism, decoupleR, and CellChat were applied to investigate differentiation trajectories, metabolic characteristics, transcription factor activities, and intercellular communication. Finally, CSC-like prognostic index (CSCLPI) was constructed and validated with multiple machine-learning approaches. The robustness and generalizability of the model were validated using the GEO and CPTAC dataset. Seven malignant epithelial subpopulations were identified. Among them, the C2 cluster displayed the highest stemness score, lowest metabolic activity, and prominent CSC-marker expression. Pseudotime analysis placed C2 at the origin of the differentiation trajectory. C2 also exhibited activation of key stemness-related transcription factors (e.g., SOX9, MYC) and robust cell-cell communication via WNT and TGF-β signaling. The CSCLPI model, derived from C2-specific genes, stratified patients into distinct risk groups. The CSCLPI score correlated significantly with tumor mutation burden, stemness indices, immune escape, and chemoresistance, and showed consistent prognostic accuracy across multiple cohorts. A nomogram incorporating CSCLPI and clinical parameters demonstrated strong prognostic value in pancreatic cancer. This study systematically characterized the heterogeneity and functional landscape of CSC-related subpopulations in pancreatic cancer. The stemness-based CSCLPI model offers a robust prognostic tool and potential therapeutic targets for personalized treatment and CSC-directed strategies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.