Research Article: Preliminary construction and validation of a prognostic prediction model for cervical cancer based on tumor mechanics-related genes
Abstract:
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.
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).
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).
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.
Introduction:
Cervical cancer (CC) ranks as the fourth most frequent malignancy among women, with an estimated 600,000 new cases and 340,000 deaths annually ( 1 ). Despite advances in screening programs that have reduced CC incidence, survival rates remain low for individuals diagnosed at advanced stages ( 2 ). Thus, identification of effective biomarkers and prognostic signatures to improve CC outcomes is a critical unmet need. The role of tumor biology has gained increasing attention in the context of tumor development.…
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