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Background <p>Nicotinamide metabolism plays a critical role in the formation and progression of ovarian cancer (OC). This study aimed to develop a prognostic marker for OC, focusing specifically on nicotinamide metabolism pathways, using multiple machine learning approaches.</p> Methods <p>Gene expression data from TCGA training cohort (n = 427) and two independent validation cohorts: GSE19829 (n = 28) and ICGC-OV (n = 112), were analyzed. A consensus prognostic model was constructed from a combination of 112 machine learning algorithms. Protein-protein interaction networks, survival analysis, gene set enrichment, mutation profiling, and immune infiltration assessments were performed. Single-cell data from GSE147082 (n = 6)were used for quality control, cell annotation, and developmental trajectory analysis.</p> Results <p>Our results identify stable prognostic genes associated with OC and developed a risk scoring system that effectively stratifies patients into high- and low-risk groups with significant survival differences (<i>p</i> &lt; 0.05). A final prognostic model base on 23 hub genes demonstrated a robust mean concordance index (C-index) of 0.709. Furthermore, we reveal significant disparities in immune checkpoint markers expression between different risk groups, underscoring potential therapeutic implications. Finally, the model is tested on additional cancer types and clinical utility is assessed using calibration and decision curve analysis.</p> Conclusions <p>This multiple machine learning tool develops a prognostic marker related to Nicotinamide metabolism for individuals with OC and could potentially be applied to other cancers in the future.</p>

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Prognostic marker related to nicotinamide metabolism in ovarian cancer based on multiple machine learning approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Xue Yang,
  • Linyan Zhu,
  • Kejun Xu,
  • Xiuyi Lv,
  • Jiaqing Zhou,
  • Huiqing Ding,
  • Xiaojiao Zheng

摘要

Background

Nicotinamide metabolism plays a critical role in the formation and progression of ovarian cancer (OC). This study aimed to develop a prognostic marker for OC, focusing specifically on nicotinamide metabolism pathways, using multiple machine learning approaches.

Methods

Gene expression data from TCGA training cohort (n = 427) and two independent validation cohorts: GSE19829 (n = 28) and ICGC-OV (n = 112), were analyzed. A consensus prognostic model was constructed from a combination of 112 machine learning algorithms. Protein-protein interaction networks, survival analysis, gene set enrichment, mutation profiling, and immune infiltration assessments were performed. Single-cell data from GSE147082 (n = 6)were used for quality control, cell annotation, and developmental trajectory analysis.

Results

Our results identify stable prognostic genes associated with OC and developed a risk scoring system that effectively stratifies patients into high- and low-risk groups with significant survival differences (p < 0.05). A final prognostic model base on 23 hub genes demonstrated a robust mean concordance index (C-index) of 0.709. Furthermore, we reveal significant disparities in immune checkpoint markers expression between different risk groups, underscoring potential therapeutic implications. Finally, the model is tested on additional cancer types and clinical utility is assessed using calibration and decision curve analysis.

Conclusions

This multiple machine learning tool develops a prognostic marker related to Nicotinamide metabolism for individuals with OC and could potentially be applied to other cancers in the future.