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Abstract

Introduction

To investigate the causal relationship between 1-palmitoyl-GPG (16:0) and serous ovarian cancer (SOC), and explore the underlying mechanisms.

Method

Two-sample Mendelian randomization (MR) and mediation effect analyses were employed to determine the causal effects of 1-palmitoyl-GPG (16:0) on serous ovarian cancer (SOC), focusing particularly on naive CD4+ T cell proportions as potential mediators. Single-cell RNA sequencing, immune infiltration analysis, and bulk machine learning algorithms were also integrated to examine the expression and impact of palmitoyl-CoA synthesis genes in CD4+ T cells. Lasso regression was utilized to refine the set of marker genes, and CatBoost machine learning algorithm was applied for predictive modeling. SHAP analysis was performed to interpret the model results.

Results

MR and mediation analyses indicated that 1-palmitoyl-GPG (16:0) has a causal effect on SOC, partly mediated by the proportion of naive CD4+ T cells, and partly through direct effects potentially involving metabolic gene expression (., PIGB) in CD4+ T cells. Single-cell and immune infiltration analyses confirmed that key palmitoyl-CoA synthesis genes, including PIGB, were highly expressed in CD4+ T cells and may contribute to SOC both indirectly, by influencing naive CD4+ T cell proportions, and directly through metabolic modulation within CD4+ subsets. The bulk RNA-seq machine learning model showed good predictive performance on an independent validation dataset. SHAP analysis was used to interpret feature contributions, with PIGB having the greatest impact on model predictions. The immune-related genes, including upregulated PIGB, GZMA, PRF1, S100A4, and CCL5, while downregulated AHNAK and LGALS1 (except in fibroblasts). Furthermore, different patterns of gene expression were observed in different CD4+ T cell clusters, which corresponded to various developmental statuses and functional roles. We identified a causal relationship between 1-palmitoyl-GPG (16:0) and SOC, which is mediated by naive CD4+ T cells and key synthesis genes.

Conclusion and Discussion

Our findings provide new insights into the metabolic and immunological mechanisms underlying SOC, and highlight potential targets for therapeutic interventions.

© 2026 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2026-01-12
2026-01-31

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Exploring CD4+ T Cell-Mediated Metabolism in Serous Ovarian Cancer via Mendelian Randomization and Single-Cell RNA-Sequencing
Bentham Science Publishers ; https://doi.org/10.2174/0115680266403606251027080335
/content/journals/ctmc/10.2174/0115680266403606251027080335
/content/journals/ctmc/10.2174/0115680266403606251027080335
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  • Article Type:     Research Article
Keywords: Mendelian randomization ; serous ovarian cancer ; metabolic ; CD4+ T cells ; scRNA-seq ; machine learning

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