Related Experiment Video
Updated: May 12, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Lactylation-related risk model for prognostication and therapeutic responsiveness in uterine corpus endometrial
Yupeng Yin1, Min Luo2,3
1Department of Obstetrics and Gynecology, General Hospital of Southern Theatre Command, Guangzhou, 510010, China.
Background:
Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological cancer characterized by varied clinical outcomes and responses to treatment. Developing effective prognostic models is essential for guiding clinical decision-making. Recent research indicates that lactylation-a process impacting gene expression and immune responses-can affect tumor growth, metastasis, and immune evasion through histone modification. This study introduces a lactylation-related risk model aimed at predicting UCEC prognosis and providing insights into treatment efficacy.
Methods:
We analyzed transcriptomic data from The Cancer Genome Atlas (TCGA) for UCEC patients and identified two distinct lactylation-related patterns using consensus clustering. A risk model developed using Cox and Lasso regression has been studied for its ability to predict prognosis, immune cell infiltration, and treatment response. Additionally, we investigated the relationship between IGSF1 gene expression and clinical features. Gene Set Enrichment Analysis (GSEA) was performed to explore the function of the IGSF1 gene.
Results:
Two distinct lactylation-related clusters were identified, along with 156 differentially expressed genes between these clusters that are associated with the prognosis of UCEC. A risk model was developed based on three genes: IGSF1, ZFHX4, and SCGB2A1. This model effectively predicts clinical characteristics of UCEC patients, including immune cell infiltration, genetic variations, drug sensitivity, and response to immunotherapy. Notably, IGSF1 is linked to poor prognosis and is associated with immune activity, tumorigenesis, and cancer metabolism.
Conclusions:
This study demonstrates that the lactylation-related risk model plays a crucial role in predicting prognosis and the efficacy of immunotherapy in UCEC, offering valuable insights for personalized treatment approaches.
