Identification and liquid-liquid phase separation-Related Genes Prognosis Model in pancreatic cancer by comprehensive analysis
Authors: Sun Yuan , Zhang Anlan , Li Yan , Hu Tao , Li Ming 
Published: December 2024

Abstract

Background Pancreatic cancer is one of the most aggressive and deadly malignancies of the digestive system, with an extremely low five-year survival rate, making treatment highly challenging. Liquid-liquid phase separation (LLPS) is a mechanism that regulates the dynamic aggregation of intracellular proteins and nucleic acids. In cancer, tumor-associated proteins and gene mutations can regulate the formation of LLPS, influencing the quantity and functionality of intracellular condensates, thereby promoting abnormal cellular behavior and contributing to tumorigenesis and cancer progression. Although previous studies have suggested that LLPS may be involved in cancer progression by affecting mechanisms such as stress response and protein aggregation, its specific role in pancreatic cancer remains unclear. Therefore, exploring the molecular mechanisms of LLPS in pancreatic cancer could uncover new biomarkers and potential therapeutic targets, facilitating the development of personalized treatment strategies.Methods LLPS-related signatures were obtained from the TCGA database and Gene Cards. Over 100 machine learning methods were applied to screen for prognostic gene sets closely related to survival, followed by the construction of a risk model. External validation of the model was performed using the GEO database. Single-cell RNA sequencing analysis was conducted on the GSE155698 dataset to assess gene expression profiles and risk scores.Result A total of 55 LLPS-related prognostic genes were identified in pancreatic cancer. Through multiple machine learning methods, 11 key prognostic genes (CKB, PRSS3, KRT6A, DLGAP5, EPHA2, FAM83B, FOXM1, IGF2BP3, KRT16, CASP14, and TOP2A.) were selected, demonstrating high sensitivity and specificity as biomarkers for the diagnosis and prognosis of PAAD patients. The risk score signature constructed based on machine learning was found to be an independent factor associated with high mortality, advanced clinicopathological features, and chemotherapy resistance. Furthermore, this signature was closely related to lipid biosynthesis pathways, cell cycle-related pathways, and KRAS/TP53 mutation profiles, suggesting its potential role in enhancing the number and functionality of abnormal lipid-based "droplets" and accelerating cell cycle progression, thereby driving pancreatic cancer development. Additionally, the signature was strongly associated with immune-suppressive cell infiltration and immune checkpoint expression, indicating its role as a key immunosuppressive factor in the tumor microenvironment. Single-cell RNA sequencing revealed that PKP3, EPHA2, and KRT16 were specifically expressed in pancreatic ductal epithelial cells.Conclusions This study provides insights into the potential links between LLPS-related molecular characteristics and clinical features, the tumor microenvironment, and clinical drug response. It highlights the crucial role of LLPS in pancreatic cancer progression and treatment resistance, offering valuable new perspectives for the prognosis and treatment response prediction in PAAD.