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已发表论文
宫颈癌中细菌脂多糖相关分子亚型的鉴定及四基因预后风险模型的建立
Authors Tong Y, Xu L, Sun Y, Zhang K, Fu X
Received 10 June 2025
Accepted for publication 5 September 2025
Published 13 September 2025 Volume 2025:17 Pages 2979—2998
DOI https://doi.org/10.2147/IJWH.S537092
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Matteo Frigerio
Yuehong Tong,1 Lili Xu,1 YiQun Sun,2 Keke Zhang,1 Xiaoyan Fu3
1Department of Gynaecology, Affilitated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321000, People’s Republic of China; 2Department of Gynaecology, Jinhua Maternal and Child Health Care Hospital, Jinhua, Zhejiang, 321000, People’s Republic of China; 3Medical Molecular Biology Laboratory, Medical College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, 321000, People’s Republic of China
Correspondence: Xiaoyan Fu, Medical Molecular Biology Laboratory, Medical College, Jinhua University of Vocational Technology, No. 1188, Wuzhou Street, Wucheng District, Jinhua, Zhejiang, 321000, People’s Republic of China, Tel +86-13586976775, Email XiaoyanFu1010@163.com
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.
Plain Language Summary: In this study, four characteristic genes that can be used as prognostic biomarkers for cervical cancer were identified, namely CXCL1, HLA-DRA, POSTN and TGFBI.The study identified two cervical cancer molecular subtypes associated with bacterial lipopolysaccharide and established a prognostic risk model with strong predictive power.Patients classified as low-risk demonstrate better survival outcomes, increased immune cell infiltration, and greater responsiveness to immunotherapy than those in the high-risk group.
Keywords: cervical cancer, bacterial lipopolysaccharide-related genes, molecular subtypes, prognostic models, immunoassays
1Department of Gynaecology, Affilitated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, 321000, People’s Republic of China; 2Department of Gynaecology, Jinhua Maternal and Child Health Care Hospital, Jinhua, Zhejiang, 321000, People’s Republic of China; 3Medical Molecular Biology Laboratory, Medical College, Jinhua University of Vocational Technology, Jinhua, Zhejiang, 321000, People’s Republic of China
Correspondence: Xiaoyan Fu, Medical Molecular Biology Laboratory, Medical College, Jinhua University of Vocational Technology, No. 1188, Wuzhou Street, Wucheng District, Jinhua, Zhejiang, 321000, People’s Republic of China, Tel +86-13586976775, Email XiaoyanFu1010@163.com
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.
Plain Language Summary: In this study, four characteristic genes that can be used as prognostic biomarkers for cervical cancer were identified, namely CXCL1, HLA-DRA, POSTN and TGFBI.The study identified two cervical cancer molecular subtypes associated with bacterial lipopolysaccharide and established a prognostic risk model with strong predictive power.Patients classified as low-risk demonstrate better survival outcomes, increased immune cell infiltration, and greater responsiveness to immunotherapy than those in the high-risk group.
Keywords: cervical cancer, bacterial lipopolysaccharide-related genes, molecular subtypes, prognostic models, immunoassays