Objective <p>Liquid–liquid phase separation (LLPS) is the membrane-less formation of functional assemblies from specific molecules. It plays a key role in tumor growth by governing gene expression. This study aims to identify LLPS-associated molecular subtypes and prognostic biomarkers in gastric cancer (GC).</p> Methods <p>LLPS-associated genes, gene expression profiles and clinical data for GC were sourced from public databases. Differential expression analysis, UpSetR package, and Cox regression analyses were employed to pinpoint LLPS-related biomarkers. Consensus clustering was employed to confirm GC molecular subtypes. Mendelian randomization (MR) was applied to uncover causal links between biomarkers and GC risk. The quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) gauged biomarker expression in GC cells (BGC-823, MGC-803, and MKN-45) and normal gastric epithelial cells (GES-1).</p> Results <p>Totally 62 LLPS-associated genes were detected. Subsequently, three LLPS-associated subtypes of GC were confirmed, containing subtypes 1, 2 and 3, and patients in subtype 2 had significantly worse overall survival&#xa0;(OS) than subtypes 1 and 3. Furthermore, three LLPS-associated biomarkers (DACT1, PTPN6, and GLE1) were identified for prognostic model construction. Kaplan–Meier (KM) curve suggested that OS was significantly shorter in low-expression of GLE1 and PTPN6 groups than in high-expression groups, respectively, while an opposite trend was observed in DACT1. Notably, MR analysis suggested that DACT1 was associated with the risk of GC. Moreover, qRT-PCR results demonstrated that PTPN6 and GLE1 were significantly higher expressed in GC cell lines compared to GSE-1 cells, whereas the opposite was true for DACT1.</p> Conclusion <p>This work detected three LLPS-associated prognostic biomarkers (DACT1, PTPN6, and GLE1) for GC. These findings were expected to lead to the development of more precise and effective therapeutic strategies for GC in the future.</p>

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Development and validation of liquid–liquid phase separation-associated prognostic biomarkers in gastric cancer

  • Donghui Liu,
  • Mingzhu Liu,
  • Juan Du

摘要

Objective

Liquid–liquid phase separation (LLPS) is the membrane-less formation of functional assemblies from specific molecules. It plays a key role in tumor growth by governing gene expression. This study aims to identify LLPS-associated molecular subtypes and prognostic biomarkers in gastric cancer (GC).

Methods

LLPS-associated genes, gene expression profiles and clinical data for GC were sourced from public databases. Differential expression analysis, UpSetR package, and Cox regression analyses were employed to pinpoint LLPS-related biomarkers. Consensus clustering was employed to confirm GC molecular subtypes. Mendelian randomization (MR) was applied to uncover causal links between biomarkers and GC risk. The quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) gauged biomarker expression in GC cells (BGC-823, MGC-803, and MKN-45) and normal gastric epithelial cells (GES-1).

Results

Totally 62 LLPS-associated genes were detected. Subsequently, three LLPS-associated subtypes of GC were confirmed, containing subtypes 1, 2 and 3, and patients in subtype 2 had significantly worse overall survival (OS) than subtypes 1 and 3. Furthermore, three LLPS-associated biomarkers (DACT1, PTPN6, and GLE1) were identified for prognostic model construction. Kaplan–Meier (KM) curve suggested that OS was significantly shorter in low-expression of GLE1 and PTPN6 groups than in high-expression groups, respectively, while an opposite trend was observed in DACT1. Notably, MR analysis suggested that DACT1 was associated with the risk of GC. Moreover, qRT-PCR results demonstrated that PTPN6 and GLE1 were significantly higher expressed in GC cell lines compared to GSE-1 cells, whereas the opposite was true for DACT1.

Conclusion

This work detected three LLPS-associated prognostic biomarkers (DACT1, PTPN6, and GLE1) for GC. These findings were expected to lead to the development of more precise and effective therapeutic strategies for GC in the future.