ArticleDiscover oncology2025
Lactylation-related risk model for prognostication and therapeutic responsiveness in uterine corpus endometrial carcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- The enzymatic mechanisms of lactylation and its role in gynecological diseases: a comprehensive review.Frontiers in immunology · 2026Review
- Lactylation-related multigene signature in multiple myeloma: integrated prognostic stratification, immune landscape profiling, and therapeutic guidance.Immunologic research · 2025Article
- Decoding protein lactylation in the pathogenesis and progression of gynecological cancer.American journal of cancer research · 2025Review
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2 authors.
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Abstract
backgroundUterine 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.
methodsWe 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.
resultsTwo 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.
conclusionsThis 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.
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