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<p>Programmed cell death protein 1 (PD-1) plays a critical role in immune tolerance and evasion within the tumor microenvironment, and anti-PD-1 immunotherapy has shown efficacy in treating advanced melanoma. However, response rates vary significantly among patients, necessitating the identification of reliable biomarkers to predict treatment efficacy. Based on within-sample relative expression orderings, we analyzed RNA sequencing data from melanoma patients to construct a predictive model comprising gene pairs associated with treatment response. The model’s performance was validated across multiple independent datasets and assessed for correlations with immune infiltration and survival outcomes. The constructed 15-pair model achieved a prediction accuracy of 100% in training datasets and 89.47% in validation sets. Validation in melanoma patients lacking treatment response data revealed significant differences between predicted responders and non-responders across datasets, with the model being an independent prognostic factor. Increased immune cell infiltration was observed in responders, correlating with higher expression levels of key immune checkpoint genes. The relative expression orderings-based model shows promise as a tool for predicting responses to anti-PD-1 therapy in melanoma patients, supporting personalized treatment strategies.</p>

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Relative expression orderings based prediction of treatment response to Anti-PD-1 immunotherapy in advanced melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月25日

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  • Yaru Gao,
  • Yue Huo,
  • Lingli Wang,
  • Jiayi Ruan,
  • Lanzhen Chen,
  • Hongdong Li,
  • Guini Hong

摘要

Programmed cell death protein 1 (PD-1) plays a critical role in immune tolerance and evasion within the tumor microenvironment, and anti-PD-1 immunotherapy has shown efficacy in treating advanced melanoma. However, response rates vary significantly among patients, necessitating the identification of reliable biomarkers to predict treatment efficacy. Based on within-sample relative expression orderings, we analyzed RNA sequencing data from melanoma patients to construct a predictive model comprising gene pairs associated with treatment response. The model’s performance was validated across multiple independent datasets and assessed for correlations with immune infiltration and survival outcomes. The constructed 15-pair model achieved a prediction accuracy of 100% in training datasets and 89.47% in validation sets. Validation in melanoma patients lacking treatment response data revealed significant differences between predicted responders and non-responders across datasets, with the model being an independent prognostic factor. Increased immune cell infiltration was observed in responders, correlating with higher expression levels of key immune checkpoint genes. The relative expression orderings-based model shows promise as a tool for predicting responses to anti-PD-1 therapy in melanoma patients, supporting personalized treatment strategies.