Evidence map›Paper›PMID 42318471›Full record

ArticleFrontiers in oncology2026

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

Lu Zhang, Haonan Fu, Yali Feng, Jianxin Tian, Xue Gao, Yuhong Shang

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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Lu ZhangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Haonan FuDepartment of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Yali FengDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Jianxin TianDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Xue GaoDepartment of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
Yuhong ShangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

cervical cancerMMP1prognostic modelstrain elastographytumor mechanicstumor stiffness

Identifiers

PMID42318471
PMCPMC13272372

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.