Effects of Telomere Related Senescence-Genes in Cervical Cancer.

Authors

Cong Xu, Yonghong Xu, Qingcao, Guoling Luo, Jingwen Yu, Huaqiu Chen, Guangming Wang

Published in

Reproductive sciences (Thousand Oaks, Calif.). Oct 09, 2026. Epub Oct 09, 2026.

Abstract

Globally, cervical cancer is a prevalent cancer among women and ranks as the fourth leading cause of mortality in gynecological cancers. However, how telomeres affect cervical cancer remains uncertain. The research entailed the procurement of telomere-associated genes (TRGs) from TelNet. The Cancer Genome Atlas (TCGA) database provided the clinical data and TRG expression rates in patients with cervical cancer. Within the TCGA-CESC dataset, 327 TRGs were identified, differentiating between cancerous and healthy tissues. Subsequently, we combined genes linked to aging and telomeres, leading to 31 overlapping genes and performing an analysis of functional enrichment on these essential genes. Genes unique to telomeres and closely associated with cervical cancer play various roles, mainly in the cell cycle, DNA replication, and DNA repair processes. Key genes, such as those associated with cell aging, homologous recombination, and Human T-cellleukemia virus 1 infection, are vital in the life cycle of a cell. Dysfunction in these genes could lead to irregularities in cellular synthesis and apoptosis processes, potentially accelerating the progression of cervical cancer. Subsequently, the data was analyzed sequentially using single-factor Cox regression, Lasso regression, and multi-factor Cox regression techniques, leading to the development of the TRGs risk model.Among the pinpointed TCGA group (p < 0.001), cervical cancer sufferers with high-risk TRGs showed poorer outcomes. Furthermore, the TRGs risk score emerged as an independent risk factor for cervical cancer. Additionally, communities susceptible to TRGs could benefit from specific medical interventions. To conclude, our team developed a genetic risk model associated with telomeres to predict outcomes in cervical cancer patients, potentially assisting in selecting suitable treatment drugs for them.

PMID:
42855652
Bibliographic data and abstract were imported from PubMed on 10 Oct 2026.

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