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<p>Women around the world are troubled by life-threatening cervical cancer. It is urgent to identify a biomarker to improve the prognosis of cervical cancer patients. Based on gene expression profiles and single-cell sequencing data obtained from public databases, we performed dimensionality reduction and clustering analyses, Scissor analysis, WGCNA, and machine-learning modeling using 10 base algorithms and their 101 individual or combined strategies. We finally screened 24 consensus prognostic genes to develop a novel model artificial intelligence-derived prognostic signature (AIDPS), based on C-index which was detected in six validation datasets (TCGA_Test, TCGA_Entire, CGCI-HTMCP-CC, GSE39001, GSE44001, and GSE52903). AIDPS demonstrated modest but consistent prognostic performance across multiple independent cervical cancer cohorts, with an average C-index of 0.665.The accuracy of AIDPS in predicting CESC was significantly better than that of other clinical characteristics including age, pathological TNM stages, and grade. In conclusion, our study developed a consensus model AIDPS, an effective strategy to further guide the clinical management and individualized treatment of cervical cancer.</p>

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Investigation of the clinical value of artificial intelligence-derived prognostic signature in cervical cancer based on machine learning algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Chao Xu,
  • Hang Li,
  • Yuxuan Wu,
  • Qiuming Yao,
  • Yanbo Zhong

摘要

Women around the world are troubled by life-threatening cervical cancer. It is urgent to identify a biomarker to improve the prognosis of cervical cancer patients. Based on gene expression profiles and single-cell sequencing data obtained from public databases, we performed dimensionality reduction and clustering analyses, Scissor analysis, WGCNA, and machine-learning modeling using 10 base algorithms and their 101 individual or combined strategies. We finally screened 24 consensus prognostic genes to develop a novel model artificial intelligence-derived prognostic signature (AIDPS), based on C-index which was detected in six validation datasets (TCGA_Test, TCGA_Entire, CGCI-HTMCP-CC, GSE39001, GSE44001, and GSE52903). AIDPS demonstrated modest but consistent prognostic performance across multiple independent cervical cancer cohorts, with an average C-index of 0.665.The accuracy of AIDPS in predicting CESC was significantly better than that of other clinical characteristics including age, pathological TNM stages, and grade. In conclusion, our study developed a consensus model AIDPS, an effective strategy to further guide the clinical management and individualized treatment of cervical cancer.