ArticleAging2023
A cellular senescence-related genes model allows for prognosis and treatment stratification of cervical cancer: a bioinformatics analysis and external verification.
Article in Aging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular characterization of HPV integrations in high-risk HPV-positive women and development of a prognostic signature based on integration-associated genes for cervical cancer.Infectious agents and cancer · 2026Article
- A novel programmed-cell-death-related prognostic risk model for cervical cancer based on mitochondrial genes.Translational cancer research · 2026Article
- Identification of miRNA biomarkers and development of predictive signatures for early detection and prognosis in cervical precancer and cancer.Human genomics · 2026Article
- Classification of lung adenocarcinoma based on senescence-related genes identifies a cluster with immunotherapy resistance and poor prognosis.Discover oncology · 2025Article
- Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model.Journal of cancer research and clinical oncology · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundCervical cancer (CC) is highly lethal and aggressive with an increasing trend of mortality for females. Molecular characterization-based methods hold great promise for improving the diagnostic accuracy and for predicting treatment response.
methodsThe mRNAs expression data of CC patients and cellular senescence-related genes were obtained from the Cancer Genome Atlas (TCGA) and CellAge databases, respectively. Differentially expressed genes (DEGs) of senescence related genes between tumor and normal tissues were used for Least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. Univariate and LASSO regression analyses were applied to establish a predictive nomogram. The performance of the nomogram were evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. GSE44001 and GSE52903 were used for external validation.
resultsWe established a cellular senescence-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of CC patients in the TCGA database. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS,
conclusionsOur results suggested a six-senescence related signature and established a prognostic nomogram that reliably predicted the overall survival for CC. These findings may be beneficial to personalized treatment and medical decision-making.
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