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

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 (TCGATest, TCGAEntire, 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.