Cervical 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.

中文翻译：

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开发预测早期宫颈癌患者复发的基因组

宫颈癌（CC）是影响全世界女性的常见恶性肿瘤。我们的目标是利用多组学数据开发基于共识的基因组，可以有效预测早期宫颈癌患者的复发。我们利用“多组学共识整合分析（MOVICS）”包进行共识聚类设计，以整合多个组学数据集并改善早期 CC 的分子分类格局。我们将“静息和幼稚”肿瘤微环境 (TME) 确定为癌症亚型 (CS) 2。利用 CS 分类器中的特征基因，我们采用机器学习算法来识别基因组，包括 ALDH1A1、CLDN10、MUC13 和 C10orf99 ，它可以为每位患者生成一致的机器学习驱动评分（CMLS）。将患者分为高 CMLS 组和低 CMLS 组得出的 Kaplan-Meier 曲线表明两组之间的复发率存在显着差异。即使在多变量 Cox 回归分析中调整临床特征后，这种差异仍然显着，CMLS 的风险比超过了临床特征。此外，不同 CMLS 组之间的 TME 表现出显着差异，表明 CMLS 低的患者可能对免疫治疗表现出更好的反应。这项研究强调了 CMLS 方法在预测早期宫颈癌患者复发方面的潜力，并为选择适合免疫治疗的患者提供了筛选模型。