Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma
Abstract Background Uterine Corpus Endometrial Carcinoma (UCEC) is a highly heterogeneous tumor, and limitations in current diagnostic methods, along with treatment resistance in some patients, pose significant challenges for managing UCEC. The excessive activation of G2/M checkpoint genes is a cruc...
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BMC
2025-02-01
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| Series: | Cancer Cell International |
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| Online Access: | https://doi.org/10.1186/s12935-025-03667-4 |
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| author | Yiming Liu Yusi Wang Shu Tan Xiaochen Shi Jinglin Wen Dejia Chen Yue Zhao Wenjing Pan Zhaoyang Jia Chunru Lu Ge Lou |
| author_facet | Yiming Liu Yusi Wang Shu Tan Xiaochen Shi Jinglin Wen Dejia Chen Yue Zhao Wenjing Pan Zhaoyang Jia Chunru Lu Ge Lou |
| author_sort | Yiming Liu |
| collection | DOAJ |
| description | Abstract Background Uterine Corpus Endometrial Carcinoma (UCEC) is a highly heterogeneous tumor, and limitations in current diagnostic methods, along with treatment resistance in some patients, pose significant challenges for managing UCEC. The excessive activation of G2/M checkpoint genes is a crucial factor affecting malignancy prognosis and promoting treatment resistance. Methods Gene expression profiles and clinical feature data mainly came from the TCGA-UCEC cohort. Unsupervised clustering was performed to construct G2/M checkpoint (G2MC) subtypes. The differences in biological and clinical features of different subtypes were compared through survival analysis, clinical characteristics, immune infiltration, tumor mutation burden, and drug sensitivity analysis. Ultimately, an artificial neural network (ANN) and machine learning were employed to develop the G2MC subtypes classifier. Results We constructed a classifier based on the overall activity of the G2/M checkpoint signaling pathway to identify patients with different risks and treatment responses, and attempted to explore potential therapeutic targets. The results showed that two G2MC subtypes have completely different G2/M checkpoint-related gene expression profiles. Compared with the subtype C2, the subtype C1 exhibited higher G2MC scores and was associated with faster disease progression, higher clinical staging, poorer pathological types, and lower therapy responsiveness of cisplatin, radiotherapy and immunotherapy. Experiments targeting the feature gene KIF23 revealed its crucial role in reducing HEC-1A sensitivity to cisplatin and radiotherapy. Conclusion In summary, our study developed a classifier for identifying G2MC subtypes, and this finding holds promise for advancing precision treatment strategies for UCEC. |
| format | Article |
| id | doaj-art-1c8fbac1173a4b82879eb321c9e00c97 |
| institution | Kabale University |
| issn | 1475-2867 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj-art-1c8fbac1173a4b82879eb321c9e00c972025-02-09T12:55:22ZengBMCCancer Cell International1475-28672025-02-0125112210.1186/s12935-025-03667-4Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinomaYiming Liu0Yusi Wang1Shu Tan2Xiaochen Shi3Jinglin Wen4Dejia Chen5Yue Zhao6Wenjing Pan7Zhaoyang Jia8Chunru Lu9Ge Lou10Department of Gynecology, Harbin Medical University Cancer HospitalLaboratory of Medical Genetics, Harbin Medical UniversityDepartment of Gynecology, Harbin Medical University Cancer HospitalDepartment of Gynecology, Harbin Medical University Cancer HospitalDepartment of Gynecology, Harbin Medical University Cancer HospitalDepartment of Gynecology, Harbin Medical University Cancer HospitalDepartment of Gynecology, Harbin Medical University Cancer HospitalSecond Affiliated Hospital of Harbin Medical UniversitySecond Affiliated Hospital of Harbin Medical UniversityDepartment of Gynecology, Suihua Maternity and Health Care HospitalDepartment of Gynecology, Harbin Medical University Cancer HospitalAbstract Background Uterine Corpus Endometrial Carcinoma (UCEC) is a highly heterogeneous tumor, and limitations in current diagnostic methods, along with treatment resistance in some patients, pose significant challenges for managing UCEC. The excessive activation of G2/M checkpoint genes is a crucial factor affecting malignancy prognosis and promoting treatment resistance. Methods Gene expression profiles and clinical feature data mainly came from the TCGA-UCEC cohort. Unsupervised clustering was performed to construct G2/M checkpoint (G2MC) subtypes. The differences in biological and clinical features of different subtypes were compared through survival analysis, clinical characteristics, immune infiltration, tumor mutation burden, and drug sensitivity analysis. Ultimately, an artificial neural network (ANN) and machine learning were employed to develop the G2MC subtypes classifier. Results We constructed a classifier based on the overall activity of the G2/M checkpoint signaling pathway to identify patients with different risks and treatment responses, and attempted to explore potential therapeutic targets. The results showed that two G2MC subtypes have completely different G2/M checkpoint-related gene expression profiles. Compared with the subtype C2, the subtype C1 exhibited higher G2MC scores and was associated with faster disease progression, higher clinical staging, poorer pathological types, and lower therapy responsiveness of cisplatin, radiotherapy and immunotherapy. Experiments targeting the feature gene KIF23 revealed its crucial role in reducing HEC-1A sensitivity to cisplatin and radiotherapy. Conclusion In summary, our study developed a classifier for identifying G2MC subtypes, and this finding holds promise for advancing precision treatment strategies for UCEC.https://doi.org/10.1186/s12935-025-03667-4Uterine Corpus Endometrial CarcinomaG2/M CheckpointPrecision TreatmentDrug ResistanceG2MCS |
| spellingShingle | Yiming Liu Yusi Wang Shu Tan Xiaochen Shi Jinglin Wen Dejia Chen Yue Zhao Wenjing Pan Zhaoyang Jia Chunru Lu Ge Lou Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma Cancer Cell International Uterine Corpus Endometrial Carcinoma G2/M Checkpoint Precision Treatment Drug Resistance G2MCS |
| title | Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| title_full | Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| title_fullStr | Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| title_full_unstemmed | Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| title_short | Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| title_sort | characterization of g2 m checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma |
| topic | Uterine Corpus Endometrial Carcinoma G2/M Checkpoint Precision Treatment Drug Resistance G2MCS |
| url | https://doi.org/10.1186/s12935-025-03667-4 |
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