Cervical cancer (CC) ranks as the fourth leading cause of cancer-related mortality among women, with approximately 604,127 new cases and 341,831 deaths reported globally in 2020 (1). Despite the consistently high incidence of CC, it remains a preventable disease. Early diagnosis and timely intervention can effectively prevent tumor development and progression.

It is well-established that persistent infection with high-risk human papillomavirus (HR-HPV) represents a significant contributing factor in the progression of cervical intraepithelial neoplasia (CIN) to CC (2, 3). Nevertheless, it is estimated that approximately 85% to 90% of women infected with HPV achieve spontaneous viral clearance through the body’s immune response, while only 10% to 15% experience persistent infection (4, 5). Consequently, it can be hypothesized that the progression to CC may depend on the presence of additional cofactors (6). Previous studies have shown that demonstrated that cells infected with the HPV play a crucial role in creating a supportive and immunosuppressive post-infection microenvironment (PIM), which promotes viral persistence and replication by interacting with normal resident cells (7, 8). The chronic inflammatory response elicited by persistent HPV infection leads to recurrent local tissue injury and regeneration in the cervix. The accumulation of various cellular damage events ultimately contributes to the progression from CIN to CC (9, 10). Conversely, persistent HPV infection is significantly linked to modifications in the tumor immune microenvironment (TIME) (11). Several studies have suggested that an imbalance of local immune cells within the cervix may facilitate persistent HPV infection (12), with particular emphasis on the dysregulation of CD4+ and CD8+ T cell populations. The CD4+ T cell subset plays a pivotal role in anti-tumor immunity, tumor immune evasion, tolerance mechanisms, TIME and the maintenance of immune homeostasis (13). During the primary immune period following HPV infection, CD4+T cells are activated in secondary lymphoid organs, enhancing cellular or humoral immune responses to eliminate pathogens through the action of T-helper 1 (Th1) and T-helper 2 (Th2), respectively (14, 15). Dysfunctional CD4+T cells have a weaker ability to clear viruses, while the recruitment and expansion of regulatory T cells (Tregs) create a favorable immunosuppressive environment for HPV (16), leading to the long-term presence of HPV and increasing the risk of cervical disease progression and malignant transformation.

Studies indicate that the immune system has a dual role in cancer: it can both eliminate cancer cells and promote tumor growth by creating a supportive microenvironment (17, 18). As CC advances, it has the potential to create an immunosuppressive microenvironment, thereby undermining the host’s anticancer immune response. The phenomenon of immune escape is intricately linked to alterations in tumor-infiltrating immune cells (TICs) and the expression of tumor-infiltrating cell-related genes (TICRGs) within the tumor microenvironment of CC. For instance, prior research has demonstrated that the progression of CC is frequently associated with an elevated presence of regulatory T cells (Tregs) and an upregulation of the CTLA-4 gene expression (19, 20). Therefore, exploring key immune factors in cervical inflammation-cancer transformation is crucial for developing a CC predictive model. Recently, more molecules important for CC development and prognosis have been identified (21–23).

This study employed the Random Forest algorithm on public transcriptomic data to identify crucial immune factors in the cervical “inflammation-cancer transition” and create a predictive model, validated with internal and external data. multiplexed immunohistochemistry (mIHC) (24) was used to assess immune-related gene and cell expression in clinical samples, confirming the model’s reliability. Our goal is to analyze the gene expression levels and immune cell infiltration status in HPV-infected patients. This analysis will help assess their likelihood of developing cervical cancer, enabling early diagnosis and treatment by clinicians.

CC is a prevalent gynecological tumor posing a significant threat to women’s health. The progression risk of CIN I to CC varies by grade: 60% of CIN I lesions resolve on their own, 11% advance to carcinoma in situ, and only 1% become invasive cancer. For CIN II and CIN III lesions, 5% and 12%, respectively, progress to invasive cancer (29). Early-stage CIN can be effectively treated with ablation (cryotherapy or thermal ablation) or excision (large ring excision or cold knife cone). However, it should not be ignored that simple cytological examination cannot accurately predict the potential of CIN to progress to CC. Thus, a predictive model for “inflammatory cancer transformation” is needed to aid clinicians in forecasting disease progression.

Persistent HR-HPV infection primarily causes CIN and CC, by using viral oncoproteins E6 and E7 to deactivate tumor suppressor genes p53 and pRB. This disruption of cell cycle control allows unchecked cervical cell division, promoting CIN and CC development and progression (30). Precisely because the body’s immune system is unable to completely clear the HPV virus, CC are infiltrated by a variety of TICs, which promote carcinogenesis (31, 32). Previous studies have shown that during the progression of CIN to CC, TICs in TIME are gradually dominated by CD8+ T cells and macrophages, and the CD4/CD8 ratio is reversed, which implies a decrease in the body’s anti-tumor immunity (33). On the other hands CC cells can induce the production of antigen-presenting cells, thus creating an immunosuppressive microenvironment that favors the survival of tumor cells (34). Meanwhile, TICRGs have an important role in tumorigenesis and tumor microenvironment formation, and their inactivation or upregulation may be associated with immune escape (35). It has been shown that TICRGs can progress CIN to CC by mediating inflammation and immune escape, and that certain methylated DNAs play a key role in controlling different transcriptional profiles in memory lymphocytes (36). In addition, these epigenetic mechanisms may involve antigen presentation, self/non-self-discrimination, and the balance between tolerance and autoimmunity (37). Therefore, identifying immune factors crucial to CIN’s progression to CC is vital for CC’s preventive diagnosis.

In this study, we identified six immune-related factors crucial for the progression from CIN to CC using public databases. The ARG2 gene, which encodes L-arginine acylase, is linked to cervical lesion progression and severity (38). Overexpression of ARG2 promotes CC cell proliferation and invasion while inhibiting apoptosis. This is accomplished by regulating L-arginine metabolism and modulating the tumor immune microenvironment. HSP90AA1, a molecular chaperone protein is significantly overexpressed in CC tissues compared to normal cervical tissues, especially in advanced CC (39), and is closely associated with the biological behaviors of tumor cells, including proliferation, metastasis, and drug resistance (40–42). The utilization of HSP90 inhibitors has been demonstrated to impede the proliferation and migration of CC cells, while simultaneously enhancing the sensitivity of radiation therapy (43). EZH2, a histone methyltransferase involved in chromatin modification and gene transcription, is associated with higher tumor malignancy, differentiation, and metastasis in CC (44–46),. The available evidence (47, 48) indicates that elevated EZH2 expression is associated with enhanced proliferation, invasion, and metastasis of CC cells, and is linked to a poor prognosis. ICAM1 is a cell surface protein that aids immune cells in identifying and destroying tumor cells. Its high expression boosts immune cell activity and infiltration, enhancing tumor surveillance. ICAM1 binds to ligands like LFA-1 and MAC-1, which play roles in inflammation and tumor metastasis (49–51).

M1 macrophages are immune cells that can participate in the regulation of the tumor microenvironment through the production of cytotoxins, chemokines, and inflammatory mediators, which play an important role in tumor growth and metastasis. It has been demonstrated (52) that in the tumor microenvironment, M1 macrophages may be polarized to M2 macrophages by certain factors secreted by tumor cells, including IL-10 and TGF-β, which consequently facilitate tumor growth and metastasis. Activated CD4 memory T cells state play a pivotal role in tumorigenesis, and these cells are capable of recognizing and attacking tumor cells, thereby playing a role in immune surveillance and tumor clearance (53). Accordingly, enhancing the efficacy of immune surveillance by activated CD4 memory T cells may represent a pivotal strategy for counteracting tumor immune evasion.

This study developed a prediction model using the random forest algorithm (54), evaluating its efficiency with the AUC under the ROC curve. 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. To validate the model’s effectiveness, this study tested six predictors in clinical cervical disease samples using mIHC experiments. These results served as external validation to confirm the model’s reliability. The findings indicate that the prediction model for “inflammation-cancer transition,” based on 4 TICRGs and 2 TIPs predictors, performed well in both internal and external cohorts.

Although the random forest algorithm has been widely applied in many fields, it still faces some challenges. Firstly, although random forests effectively reduce the risk of overfitting by integrating multiple trees, overfitting may still occur in situations with high data noise or small sample sizes. To solve this problem, model performance can be optimized by limiting the maximum depth of the tree, increasing the minimum number of sample splits or minimum number of sample leaves, and adjusting hyperparameters through cross validation. Secondly, the decision-making process of random forests is relatively complex, resulting in poor interpretability. To address this limitation, the contribution of variables can be evaluated through feature importance, or a visualization tool can be used to interpret a single decision tree to enhance the interpretability of the model. In addition, parameter selection and optimization of random forests is an important research direction, which can be automatically optimized through grid search or randomized search to improve model performance. Finally, random forests require a significant amount of computational resources when training multiple trees, especially when dealing with large-scale datasets or high-dimensional data, resulting in high computational costs. To solve this problem, a balance between performance and efficiency can be found by reducing the number of trees, or by dimensionality reduction and feature selection of the data to reduce the number of features, thereby reducing computational complexity. Through the above methods, the limitations of the random forest algorithm in practical applications can be effectively alleviated, further improving its performance and practicality.

In mIHC experiments, protein levels of HSP90AA1, ICAM1 and numbers of macrophage M1, and activated T cells CD4 memory increased with disease severity, consistent with bioinformatics findings. HSP90AA1 aids antigen release from cancer cells in TIME, while EZH2, STAT1, and ICAM1 hinder immune cell infiltration. Activated CD4 memory T cells promote inflammation and push CIN to CC. And M1 macrophages have anti-inflammatory effects. As the disease progresses, the anti-inflammatory effect of macrophages increases. At the same time, we can see that in the later stages of disease development, Macrophage M1 cannot play a good role in tissue repair, and Macrophage M1 will switch to Macrophage M2. The level of ARG2 protein increases as the disease progresses from CIN III to CC, supporting the view that ARG2 mRNA expression is significantly upregulated in women with cancer lesions. Conversely, EZH2 levels peaked at the CIN III stage and dropped significantly in CC, contradicting bioinformatics predictions of a steady increase, with the highest levels in CC. The cause of this discrepancy remains unknown. Meanwhile, we compared CIN III and CC groups and found significant differences in HSP90AA1, ICAM1, EZH2, ARG2, and macrophage M1, indicating these may be biomarkers for predicting CIN III’s progression to CC.

Although the model in this study has shown some predictive ability in preliminary validation, there are still limitations: 1. This study mainly relies on transcriptomic data, which may not fully capture the complexity of cervical cancer progression despite providing comprehensive information on gene expression levels. Therefore, future research should integrate multiple omics data to more comprehensively reveal the molecular mechanisms of cervical cancer. 2. The current research sample may have selection bias and lack patient data from different races, regions, and economic backgrounds. Therefore, future research should expand the sample size and include more diverse patient populations to ensure the universality and robustness of the model. 3. Current research is mainly based on correlation analysis and lacks support from functional experiments. Therefore, future research should further validate the functions of these genes through in vitro and in vivo experiments, and explore their specific mechanisms of action in the progression of CIN, in order to provide stronger theoretical basis for the early diagnosis and treatment of cervical cancer.

In this study, we developed a prediction model for the “inflammation-cancer transition” using six predictors: ARG2, HSP90AA1, EZH2, ICAM1, macrophage M1, and activated CD4 memory T cells. The model showed good predictive efficacy, as evaluated by the area under the ROC curve. The model showed strong predictive performance in both validation and experimental cohorts, suggesting it can somewhat predict CIN progression to CC. Expression levels of HSP90AA1, EZH2, ICAM1, and macrophage M1 increased progressively across the four cervical lesion stages, with significant intergroup differences. These findings indicate that the biomarkers could be useful in clinical settings. ARG2 showed a steady decline across cervical lesion stages, suggesting it might protect against CC. This model could help predict patient immune status and disease progression, enabling timely interventions at the CIN or early CC stages. Additionally, it may provide clinicians with new insights for diagnosing cervical diseases.