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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1532048
This article is part of the Research Topic Prognostic Biomarkers and Gene Signatures in Endometrial, Ovarian, and Cervical Cancer View all 11 articles
A predictive model for the transformation from cervical inflammation to cancer based on tumor immune-related factors
Provisionally accepted
Wenjie Wang 1,2
HUANG CHUN TAO 2,3
Shiwen Bi 2,4
Huiting Liang 2,3
Songlin Li 4 Tingting Lu 2,3 Ben Liu 1,2
Yong Tang 3,4*
Wang Qi 2,3*- 1 Guangxi Medical University, Nanning, China
- 2 Key Laboratory of Early Prevention and Treatment of Regional High-incidence Tumors, Ministry of Education Key Laboratory,Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
- 3 Guangxi Medical University Cancer Hospital, Nanning, Guangxi Zhuang Region, China
- 4 Wuming Hospital Affiliated to Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
Persistent high-risk human papillomavirus (HR-HPV) infection is crucial in transforming cervical intraepithelial neoplasia (CIN) into cervical cancer (CC) by evading immune responses. Additionally, changes in the tumor immune microenvironment (TIME) are increasingly linked to CIN progression to CC. In this study, we used public databases to collect transcriptome data for CIN, CC, and normal cervix, employing LASSO regression to find TIP genes with differential expression. We also used the CIBERSORT algorithm to analyze immune cells in the cervix. ROC curves were plotted to assess tumor-infiltrating immune cells (TICs) and the expression of tumorinfiltrating cell-related genes (TICRGs) for predicting CC efficacy and identifying immune-related genes and cells associated with cervical disease progression for future modeling. We developed a cervical "inflammation-cancer transition" prediction model using the random forest algorithm and assessed its accuracy with internal and external data. Clinical samples from two hospitals were analyzed using multiplexed immunohistochemistry (mIHC) to detect risk factors in various cervical diseases, serving as an independent validation cohort for the model's reliability. In conclusion, the developed model enhances the predictive accuracy for the progression of CIN to CC and offers novel insights for the early diagnosis and screening of CC.
Keywords: cervical intraepithelial neoplasia (CIN), cervical cancer, tumor immune microenvironment (TIME), tumor-infiltrating immune cells (TICs), tumor-infiltrating cell-related genes, multiplexed immunohistochemistry, random forest, predictive model
Received: 21 Nov 2024; Accepted: 04 Apr 2025.
Copyright: © 2025 Wang, CHUN TAO, Bi, Liang, Li, Lu, Liu, Tang and Qi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Yong Tang, Wuming Hospital Affiliated to Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
Wang Qi, Key Laboratory of Early Prevention and Treatment of Regional High-incidence Tumors, Ministry of Education Key Laboratory,Guangxi Medical University, Nanning, 530021, Guangxi Zhuang Region, China
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