ArticleFrontiers in oncology2026
lncRNA-based prognostic model for pancreatic cancer centered on the TME with exploratory LLPS connections.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Liquid-Liquid Phase Separation (LLPS), tumor microenvironment (TME), and long non-coding RNA (lncRNA) all have varying degrees of influence on the expression regulation of tumors. However, research on the association of these three in pancreatic cancer (PC) still requires further exploration. This study seeks to establish the relationships among these three themes through bioinformatics and to identify biomarkers that can predict the prognosis of PC patients. Methods: Data sets from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) are obtained from the UCSC platform. lncRNAs associated with the LLPS and TME gene sets are screened, and model lncRNAs are identified through comprehensive analysis conducted with least absolute shrinkage and selection operator (LASSO) regression and cox proportional hazards (COX) regression. Additionally, the predictive efficacy of the model lncRNAs is validated through multiple databases and cohorts. Furthermore, the expression of the model lncRNAs is validated at a biological level. Results: A comprehensive analysis establishes an optimal combination consisting of 5 lncRNAs. The Kaplan-Meier curves and receiver operating characteristic (ROC) curves for each cohort demonstrates the effectiveness of the model lncRNAs characteristics. Additionally, the COX regression analysis of clinical characteristics and the analysis of mutation data further indicates the stability of the model lncRNAs. Furthermore, the expression levels of model lncRNAs in cell lines are consistent with the analysis results. Conclusion: The model lncRNAs identified in this study, which are correlated with LLPS and TME, demonstrate significant potential as independent biomarkers for predicting the prognosis of PC patients.
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