Development of gene panel for predicting recurrence in early‐stage cervical cancer patients

Abstract

AbstractCervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus‐based gene panel using multi‐omics data that could effectively predict recurrence in early‐stage cervical cancer patients. We utilized the “Multi‐Omics Consensus Integration Analysis (MOVICS)” package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early‐stage CC. We identified the “resting and naive” tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning‐driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan–Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early‐stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.