A Novel Liquid–Liquid Phase Separation Characteristic Model Associated with Prognosis and Immune Landscape of Gastric Cancer Patients
Authors: Miao Renjie , Liu Yun , Chen Ruiyun , Sun Zili , Zhang Wei , Li Rui , Song Rong , Wang Xuemei , Wang Jinlan , Shen Shuo 
Published: July 2024

Background
Liquid-liquid phase separation (LLPS) refers to a phenomenon in which unique liquid condensates are formed due to weak interactions among biomolecules, including proteins and nucleic acids. In cellular environments, abnormal LLPS can induce aggregation of membrane-less organelles, disrupt intracellular signaling, alter chromatin structures, and cause aberrant gene expression. The significance of LLPS in gastric cancer (GC) cells is still poorly understood. This study aims to integrate multiple omics analysis and multiple machine learning algorithms to identify LLPS characteristic genes (LCGs) which can be used to develop a LLPS characteristic prognostic model.
Methods
Transcriptomic and single-cell data for GC patients were retrieved from the GEO and TCGA databases. The LLPS gene set was extracted from the PhaSepDB database. Initial cellular localization analysis of LLPS gene set-expressing cells was performed using single-cell data from GSE167297. Subsequently, we analyzed 797 GC samples from the TCGA-STAD and GSE84437 merged cohort using the ConsensusClusterPlus method, then we subdivided the merged cohort into two clusters based on the expression of the LLPS gene set for further prognostic and immune analyses. Characteristic genes of the LLPS gene set were identified by the best combination of four machine learning algorithms correlating with patient survival status and time, which were then validated across three independent GC patient cohorts. The differential expression of LCGs in the prognostic model was validated using the HPA and UALCAN databases, as well as western blotting. Additionally, a nomogram was developed to improve the effectiveness of the model in clinical application. Furthermore, differences in the tumor immune microenvironment (TME), immunotherapy response, and drug sensitivity between different risk groups were studied through a variety of immune algorithms. Mutational analysis of ten LLPS gene set genes was conducted based on mutation data from the TCGA-STAD cohort.
Results
A LLPS characteristic prognostic model based on a combination of four machine learning algorithms was established, identifying ten LCGs with high predictive value for the prognosis, TME, immunotherapy responses, and chemotherapy drug sensitivity of GC patients. Additionally, a specific nomogram was developed, incorporating clinical features to enhance the effectiveness of the LLPS clinical score, with AUC values of 0.722, 0.715, 0.707 at 1, 3, and 5 years, respectively. The LLPS prognostic model demonstrated good predictive value for survival status across different age groups, T stages, and N stages of GC patients. Risk scores calculated from LCGs showed linear correlations with stromal scores, immune scores, TME scores, Tumor Immune Dysfunction and Exclusion (TIDE) scores, epithelial-mesenchymal transition (EMT) scores, angiogenesis scores, and tumor purity scores. Furthermore, mutations in LCGs were found to impact the survival of GC patients.
Conclusions
The LLPS characteristic prognostic model provides a new perspective for assessing the prognosis of GC patients, their responses to immunotherapy, TME and chemotherapy drug usage.