ArticleFrontiers in oncology2025
Transcriptome profiling revealed multiple circadian rhythm-related genes associated with common gynecological cancers.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- UPP1 in Cancer: Context-Dependent Roles in Metabolic Adaptation and Treatment Response.Current issues in molecular biology · 2026Review
- Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer.Journal of gastrointestinal cancer · 2026Article
- Circadian Biology and Gynecological Diseases: Genetic Mechanisms and Potential Therapeutic Targets Underlying the Causal Link Between Insomnia and Ovarian Cysts.International journal of women's health · 2026Article
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7 authors.
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Abstract
Background: Studies have shown that more than half of the human genome expression is affected by circadian rhythms, which includes genes involved in cell cycle control, DNA repair and apoptosis that are critical in cancer biology. However, the roles of circadian rhythm-related genes (CRRGs) in cervical cancer (CC) and other common gynecologic cancers remain unclear. Methods: The transcriptome data and clinical information related to CC and other common gynecologic cancers were extracted from the UCSC Xena and Gene Expression Omnibus (GEO) databases. In this study, the differentially expressed CRRGs of CC (target genes) were obtained, and the functional enrichment analysis of these target genes was performed by "clusterProfiler". Then, the biomarkers of CC were screened out to construct the survival risk model (risk score). Moreover, function and tumor micro-environment (TME) analyses in different risk groups were performed for further study of the potential mechanism of CC. Furthermore, the prognostic value and function analyses of biomarkers in three common gynecologic cancers were performed to reveal the potential agreement or heterogeneity regulations. Results: A total of 19 target genes were associated with pyrimidine metabolism. The survival risk model was constructed with six biomarkers, including APOBEC3B, CDA, HELLS, RHOB, SLC15A3, and UPP1. Among these, APOBEC3B, HELLS, and SLC15A3 were identified as positive factors, while CDA, RHOB, and UPP1 were identified as negative factors in CC. It is notable that multiple immune-related signaling pathways were associated with the clinical risk of CC, and the immunotherapy sensitivity was worse in the high-risk group. In addition, we found that most of biomarkers had the prognostic values in other common gynecologic cancers. It was notable that the mechanisms by which these biomarkers influence gynecologic cancers were associated with extracellular matrix (ECM) receptor interaction, focal adhesion, etc. Conclusion: This study identified six circadian rhythm-related biomarkers, including APOBEC3B, CDA, HELLS, RHOB, SLC15A3, and UPP1, which were associated with the prognosis of CC. The mechanisms by which these biomarkers influence gynecologic cancers were associated with ECM receptor interaction, focal adhesion, and other functions. These findings might help to deepen the understanding of the agreement or heterogeneity of CRRGs in the pathological processes of common gynecologic cancers.
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