Scholarlink

Background <p>HPV integration into the human genome is a key molecular event in the progression of cervical cancer carcinogenesis.</p> Methods <p>This study utilized samples from high-risk HPV-positive women to investigate HPV integration. Integration-associated genes were screened based on HPV-DNA integration sequencing data from our local clinical cohort, and their expression levels and prognostic value were independently validated and used for model construction in the TCGA-CESC RNA-seq dataset. Using this integration-associated prognostic risk model, cervical cancer (CC) patients from TCGA were stratified into high-risk and low-risk groups. Differences in enrichment of PI3K-AKT pathway genes and immunotherapy response were analyzed between the two groups, and functional analysis was performed on differentially expressed immune-related genes between the subgroups.</p> Results <p>The HPV integration rate gradually increases with the progression of cervical disease. The most frequent integration-related genes were <i>KLF5</i>, <i>LINC00392</i>, <i>BCL11</i>B and <i>TP63</i>. A risk model based on eight HPV integration-associated genes was developed, which could classify CC samples into high-risk groups with significantly shorter overall survival times compared to low-risk groups (<i>P</i> &lt; 0.001). ROC curve evaluation showed that the AUCs for the 3, 5, and 7-year survival rates were 0.77, 0.74, and 0.76, respectively. The significant differences in PI3K-AKT pathway gene enrichment and prediction of immune therapy response were found between two risk groups.</p> Conclusion <p>A integration-associated prognostic risk model based on genes derived from HPV integration hotspots shows significant association with patient survival and is correlated with distinct tumor microenvironment features, including PI3K-AKT pathway activity and predicted immunotherapy response.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Molecular characterization of HPV integrations in high-risk HPV-positive women and development of a prognostic signature based on integration-associated genes for cervical cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月27日

查看全文
  • Qiongying Lyu,
  • Jiahui Zhao,
  • Yurou Chen,
  • Jiaqiang Xiong,
  • Hairong Wang,
  • Juan Zhang,
  • Lihan Wang,
  • Xiaoyan He,
  • Wei Zhang

摘要

Background

HPV integration into the human genome is a key molecular event in the progression of cervical cancer carcinogenesis.

Methods

This study utilized samples from high-risk HPV-positive women to investigate HPV integration. Integration-associated genes were screened based on HPV-DNA integration sequencing data from our local clinical cohort, and their expression levels and prognostic value were independently validated and used for model construction in the TCGA-CESC RNA-seq dataset. Using this integration-associated prognostic risk model, cervical cancer (CC) patients from TCGA were stratified into high-risk and low-risk groups. Differences in enrichment of PI3K-AKT pathway genes and immunotherapy response were analyzed between the two groups, and functional analysis was performed on differentially expressed immune-related genes between the subgroups.

Results

The HPV integration rate gradually increases with the progression of cervical disease. The most frequent integration-related genes were KLF5, LINC00392, BCL11B and TP63. A risk model based on eight HPV integration-associated genes was developed, which could classify CC samples into high-risk groups with significantly shorter overall survival times compared to low-risk groups (P < 0.001). ROC curve evaluation showed that the AUCs for the 3, 5, and 7-year survival rates were 0.77, 0.74, and 0.76, respectively. The significant differences in PI3K-AKT pathway gene enrichment and prediction of immune therapy response were found between two risk groups.

Conclusion

A integration-associated prognostic risk model based on genes derived from HPV integration hotspots shows significant association with patient survival and is correlated with distinct tumor microenvironment features, including PI3K-AKT pathway activity and predicted immunotherapy response.