ORIGINAL RESEARCH article
Front. Genet.
Sec. Cancer Genetics and Oncogenomics
Development of a senescence-related lncRNA signature in endometrial cancer based on multiple machine learning models
Provisionally accepted
- 1Fujian Cancer Hospital, Fuzhou, China
- 2The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Background Senescence-related lncRNAs (srlncRNA) mediate carcinogenesis in various malignancies. However, its roles in endometrial cancer (EC) remain unknown. Our research aims to construct a predictive srlncRNA model with prognostic and therapeutic significance in EC. Methods We first downloaded the gene expression and medical information from the TCGA, as well as senescence-related lncRNAs (srlncRNAs) from the CellAge databases. Then, a co-expression network of cell senescence-related mRNA −lncRNA was explored with R. Subsequently, we performed Cox and Lasso regression and machine learning analysis to identify srlncRNAs related to the prognosis of EC and built a predictive model. Continually, we drew a nomogram to improve its ability to predict prognosis. Further, GSEA was used to explore potential mechanisms. Differences in TME, immune infiltrating cells, and checkpoints of the two risk groups were compared using GSEA and CIBERSORT. Finally, the drug sensitivity of patient-derived tumor organoids (PDOs) was investigated. Results We first built a prognostic model based on seven srlncRNAs (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT). The model, which was screened by machine learning, functioned well in three sets with good stability and accuracy. Furthermore, the nomogram based on age, grade, and risk scores could precisely predict the prognosis of EC patients. The AUC of risk scores was highest compared to other clinical parameters (AUC risk score = 0.769, AUC age =0.615, and AUC grade = 0.681). This srlncRNAs were enriched in the cell cycle, certain malignant tumors, and cancer-associated regulatory pathways. Afterward, low-risk EC patients had more immune-infiltrating cells and may benefit from anti-PD-1 and anti-CTLA4 treatment. Paclitaxel, gemcitabine, and cisplatin (all p < 0.05) may be more useful in EC patients with high expression of targeted srlncRNAs in the GDSC database. The levels of targeted srlncRNAs and drug sensitivity varied significantly among different EC PDOs. The EC-18 PDO was more resistant to three drugs, which aligned with clinical observation. Conclusion The srlncRNA signature (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT) could guide prognosis prediction and treatment choices for EC patients.
Keywords: endometrial cancer, cell senescence, Signature, Immunotherapy, prognosis, TCGA, Patient-derived tumor organoids
Received: 18 Aug 2025; Accepted: 11 Nov 2025.
Copyright: © 2025 Lin, Lei, Li, Jiang, Jiang, Guo, Cai, Ye and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yang Sun, sunyang@fjmu.edu.cn
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