ArticleFrontiers in endocrinology2024
A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multiple Clustering Algorithms for Ovarian Cancer Subtyping (MCAOCS): identifying clinical phenotypes and treatment response in critical care.Journal of ovarian research · 2026Article
- Lactate and lactylation: mechanisms, function, diseases, and therapeutic targets.Molecular biomedicine · 2026Review
- Glycolytic reprogramming in ovarian cancer: mechanisms, immune crosstalk, and therapeutic implications.Frontiers in immunology · 2026Review
- Advancements in research regarding the influence of the tumor microenvironment on the proliferation and metastasis of osteosarcoma (Review).Oncology letters · 2026Review
- Integrating pathology genomics and single-cell genomics to identify lactate metabolism-related prognostic features and therapeutic strategies for melanoma.Apoptosis : an international journal on programmed cell death · 2025Article
- Machine learning identifies inflammation-related diagnostic biomarkers for primary myelofibrosis with clinical validation.Scientific reports · 2025Article
- Application of multimodal integration to develop preoperative diagnostic models for borderline and malignant ovarian tumors.Scientific reports · 2025Article
- Triptolide targets PPP2CA/ITGA5 axis to suppress lactate-driven ovarian cancer progression.Chinese medicine · 2025Article
- YTHDF1 boosts the lactate accumulation to potentiate cervical cancer cells immune escape.Cell death & disease · 2024Article
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8 authors.
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Abstract
Introduction: Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response. Methods: Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor. Results: This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.
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