Abstract
Globally, cervical cancer ranks as a prevalent cancer among women and stands as the fourth leading cause of mortality in gynecological cancers. Yet, it's still uncertain how telomeres impact cervical cancer. This research involved acquiring telomere associated genes (TRGs) from TelNet. Clinical data and TRGs expression levels of cervical cancer patients were acquired from the Cancer Genome Atlas (TCGA) database. Within the TCGA-CESC data collection, 327 TRGs were identified between cancerous and healthy tissues, with these genes, which differ in telomeres and are closely linked to cervical cancer, playing a role in various functional processes, predominantly in the cell cycle, DNA replication, and DNA replication. Key genes such as cellular aging, repair of double-strand breaks, and the Fanconi anemia pathway, among others, play a significant role in the cell's life cycle. Dysfunction in these genes could lead to irregularities in the body's cell synthesis and apoptosis processes, potentially hastening cervical cancer's advancement. Subsequently, the data was sequentially analyzed using single-factor cox regression, lasso regression, and multi-factor cox regression techniques, culminating in the creation of the TRGs risk model. Within the discovered TCGA group (p < 0.001), patients with cervical cancer in the group at high risk of TRGs experienced worse results. Furthermore, the TRGs risk score emerged as a standalone risk element for renal cancer. Furthermore, populations vulnerable to TRGs could gain advantages from the administration of specific therapeutic medications. To sum up, our team developed a genetic risk model linked to telomeres to forecast cervical cancer patients' outcomes, potentially aiding in choosing treatment medications for these patients.