Prognostic prediction and immune landscape analysis based on m6A methylation modification and vasculogenic mimicry in cervical cancer

Background: N6-methyladenosine (m6A) modifications are known to play a key role in the development and progression of cancer. Vasculogenic mimicry (VM) is a unique mechanism that can contribute to tumor recurrence and metastasis. However, the specific association between m6A regulators (MAGs) and VM-related genes (VRGs) in cervical cancer (CC) have yet to be elucidated. Methods: Risk signatures were constructed by Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Patients were divided into high- and low-risk groups based on the median risk score, and differences in key parameters between the two groups were assessed in terms of tumor immune landscape and somatic mutations. Results: Based on univariate Cox regression analysis and LASSO regression analyses, we constructed an eight-gene prognostic signature (termed as the mVMscore). High- and low-mVMscore groups, based on median risk scores, were associated with different clinical outcomes and biological characteristics. Survival analysis further demonstrated that patients in the low-mVMscore group had a better survival rate than those in the high-mVMscore group. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) showed that immune cells were significantly enriched in the high-mVMscore group. Immune scores, estimate scores and stromal scores were lower than those of the low-risk group. Conclusions: We constructed a novel prognostic eight-gene signature (mVMscore) based on MAGs and VRGs which exhibited significant potential to predict the need for immunotherapy in patients with cervical cancer (CC). Collectively, our findings provide a new direction for assessing the prognosis of patients with CC and designing immunotherapy strategies.