Identification of Bacterial Lipopolysaccharide-Related Molecular Subtypes and Development of a Four-Gene Prognostic Risk Model in Cervical Cancer.

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.