A predictive model for the transformation from cervical inflammation to cancer based on tumor immune-related factors - Wang, Wenjie; Huang, Chuntao; Bi, Shiwen; Liang, Huiting; Li, Songlin; Lu, Tingting; Liu, Ben; Tang, Yong; Wang, Qi (Frontiers in Immunology, 2025)
IntroductionPersistent 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.MethodsIn 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 tumor-infiltrating 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.ResultsFour genes, ARG2, HSP90AA1, EZH2, ICAM1, and two immune cells, M1 macrophages and activated CD4 memory T cells, were selected as variables, and a predictive model was constructed. The model achieved an AUC of 1 for internal training sets and 0.912 for testing sets. For validation cohort, the AUC was 0.864 for GSE7803 and 0.918 for TCGA/GTEx. For external validation (GSE39001, GSE149763, and GSE138080), the AUC was 0.703, 0.889 and 0.696. At the same time, the mIHC experimental results also effectively validated the stability of the model.DiscussionIn 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.