Tong Yuehong, Xu Lili, Sun YiQun, Zhang Keke, Fu Xiaoyan
Department of Gynaecology, Affilitated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321000, People's Republic of China.
Department of Gynaecology, Jinhua Maternal and Child Health Care Hospital, Jinhua, Zhejiang, 321000, People's Republic of China.
Int J Womens Health. 2025 Sep 13;17:2979-2998. doi: 10.2147/IJWH.S537092. eCollection 2025.
Cervical cancer (CC) ranks among the top causes of cancer-related illness and death in women worldwide. Bacterial lipopolysaccharide-related genes (LRGs) contribute to tumor progression and immunosuppression. This study aimed to identify CC molecular subtypes based on LRGs and construct a prognostic model to explore patient prognosis and immune features.
Transcriptomic data and corresponding clinical details for CC patients were obtained from publicly accessible resources such as The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Molecular subtypes were uncovered by applying non-negative matrix factorization (NMF) to prognostic LRGs. Significant prognostic genes were identified through Cox regression coupled with Shrinkage and Selection Operator (LASSO) analysis to build a risk model, which was then validated using an independent dataset from the Gene Expression Omnibus (GEO). RT-qPCR validated gene expression. Differences in prognosis, tumor microenvironment (TME), immune status, and tumor mutational burden (TMB) were analyzed between risk groups, and drug sensitivity predictions were performed using pRRophetic.
The study successfully identified two molecular subtypes. A prognostic model was developed based on four selected genes, with Receiver Operating Characteristic (ROC) curve analysis confirming its robust predictive performance in both the training and independent validation datasets. RT-qPCR analysis provided additional verification of the gene expression profiles. The low-risk cohort displayed a significantly more favorable outcome, along with increased infiltration of immune cells and enhanced immune scores. Furthermore, the signature genes were associated with sensitivity to multiple anticancer drugs, indicating potential therapeutic targets.
The risk model based on LRGs effectively predicts survival outcomes and immune characteristics in CC patients, providing a novel theoretical foundation for personalized treatment and immunotherapy strategies.
宫颈癌（CC）是全球女性癌症相关疾病和死亡的主要原因之一。细菌脂多糖相关基因（LRGs）促进肿瘤进展和免疫抑制。本研究旨在基于LRGs识别CC分子亚型，并构建预后模型以探索患者预后和免疫特征。
从诸如癌症基因组图谱（TCGA）和基因型-组织表达（GTEx）项目等公开可用资源中获取CC患者的转录组数据及相应临床细节。通过对预后LRGs应用非负矩阵分解（NMF）来揭示分子亚型。通过Cox回归结合套索（LASSO）分析确定显著的预后基因以构建风险模型，然后使用来自基因表达综合数据库（GEO）的独立数据集对其进行验证。RT-qPCR验证基因表达。分析风险组之间在预后、肿瘤微环境（TME）、免疫状态和肿瘤突变负荷（TMB）方面的差异，并使用pRRophetic进行药物敏感性预测。
该研究成功识别出两种分子亚型。基于四个选定基因开发了一个预后模型，受试者工作特征（ROC）曲线分析证实其在训练数据集和独立验证数据集中均具有强大的预测性能。RT-qPCR分析为基因表达谱提供了额外验证。低风险队列显示出明显更有利的结果，同时免疫细胞浸润增加且免疫评分提高。此外，特征基因与多种抗癌药物的敏感性相关，表明其为潜在的治疗靶点。
基于LRGs的风险模型有效预测CC患者的生存结果和免疫特征，为个性化治疗和免疫治疗策略提供了新的理论基础。
