Bioinformatics and Machine Learning-Based Identification of Cell Cycle-Related Genes and Molecular Subtypes in Endometrial Cancer

Endometrial cancer is a common malignant tumor in women, with rising incidence rates and an unoptimistic prognosis. DSN1 is a kinetochore protein-coding gene that affects centromere assembly and progression in cell cycles, which is associated with adverse predictions for many cancers. However, the role of DSN1 in UCEC has not yet been reported. We identified the UCEC-related gene module and obtained the differential genes. Then we constructed a diagnostic model and identified the subtype of the molecule and its association with predictions. Subsequently, we identified DSN1 as the core gene and predicted its predictive value. Furthermore, using bioinformatics methods, we found DSN1 was associated with certain clinical characteristics and experimentally validated the expression in cancer tissues of DSN1. Pathway enrichment analysis identified DSN1 as a cell cycle-associated protein, which was validated by WB. The protein interaction network also revealed DSN1 was significantly associated with NDC80. Then we explored the correlation of DSN1 and immune cells and immune cell infiltration and found that DSN1 may affect Th2 enrichment by affecting CCL7 and CCL8. Drug susceptibility analysis showed DSN1 was sensitive to cisplatin and resistant to sunitinib. In conclusion, DSN1 was a novel biomarker that contributes to prognosis and treatment. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The author(s) received no specific funding for this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The specimens of all 50 patients from the Second Affiliated Hospital of Nanchang University were pathologically identified with UCEC between December 2023 and September 2024. While the tissues were still fresh, total proteins were removed. The study was approved by the ethics committee of Nanchang University's Second Affiliated Hospital (No. Review [2023] No. (165)). This is a retrospective study. As it analyzed existing medical records and archived samples, obtaining informed consent was infeasible. The ethics committee of Nanchang University's Second Affiliated Hospital has approved the waiver of informed consent for this clinical research. And after the data is collected, information can be obtained that can identify individual participants. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable All datasets in the present study were downloaded from public databases. These public databases allowed researchers to download and analyze public datasets for scientific purposes.