Preliminary construction and validation of a prognostic prediction model for cervical cancer based on tumor mechanics-related genes

Objective Cervical cancer (CC) is the fourth most prevalent malignancy among women. The present study employed bioinformatics analyses to identify tumor mechanics-related genes (TMRGs), establish a prognostic model, and investigate the association of tumor stiffness with pivotal genes utilizing clinical samples. Methods mRNA data from the Genotype-Tissue Expression project (GTEx), The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO) were analyzed to identify tumor mechanics-related differentially expressed genes. A prognostic model was constructed via least absolute shrinkage and selection operator (LASSO) regression and validated in theGSE44001cohort. In the clinical component, individuals with CC were enrolled; the preoperative strain ratio (SR), reflecting tumor stiffness, was evaluated by strain elastography, and the expression of matrix metalloproteinase-1 (MMP1) in tumor tissues was evaluated using immunohistochemistry (IHC). Results A prognostic model was constructed using seven key genes: MMP1, DES, ARSJ, NT5E, P4HA3, CLMP, and SMARCA1. This model efficiently stratified individuals into high- and low-risk subgroups. The two groups exhibited distinct gene mutation landscapes, varying degrees of immune cell infiltration, and differential responses to chemotherapy. Spearman correlation analysis indicated a moderate positive link between MMP1 IHC scores and SR values (r=0.418, P = 0.012).Compared to the low-expression group, individuals with high MMP1expressionexhibited significantly elevated SR values(P<0.05). Conclusions The TMRG-based prognostic model demonstrated notable discriminative capacity. Clinical validation revealed a preliminary association between tumor stiffness and MMP1 expression in CC tissues, offering a new perspective for risk stratification and clinical evaluation in this malignancy.