ArticleTranslational cancer research2026
Establishment and validation of a prognostic model based on liquid-liquid phase separation-related genes in gastric cancer.
Article in Translational cancer research, 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
Background: Growing evidence suggests that the imbalance of liquid-liquid phase separation (LLPS) can alter the spatiotemporal coordination ability of biomolecular condensates, thereby playing an important role in carcinogenesis and cachexia. Gastric cancer (GC), ranking as the fifth most prevalent malignancy globally, remains lacking in systematic analysis at the GC-LLPS level within current research. This study aims to identify differentially expressed LLPS-related genes (LLPSGs) in GC and elucidate the role of LLPS in the initiation and progression of GC. Identifying the role of LLPS in carcinogenesis facilitates the development of personalized treatment strategies. Methods: The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) dataset was employed as the training cohort, encompassing RNA sequencing data from 375 GC samples and 32 normal samples, along with comprehensive clinical information from 443 GC patients. Differentially expressed genes associated with GC were identified, and LLPS genes correlated with overall survival (OS) in GC patients were determined using the LLPS database. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct LLPS-based prognostic models, validated using the GEO15459 dataset containing clinical and gene expression data from 192 GC patients. Model accuracy was assessed via area under the curve (AUC) values. Multiple algorithms were employed to calculate immune cell infiltration scores for high- and low-risk groups. Finally, gene set enrichment analysis (GSEA) enrichment analysis was performed on the selected genes to explore biological processes and pathways. Results: Through univariate Cox analysis and the LASSO Cox penalized regression analysis, six genes ( Conclusions: Our research establishes and validates an LLPS-associated risk model centered on the genes
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