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已发表论文
基于免疫原性细胞死亡和内质网应激相关基因特征的卵巢癌预后列线图
Authors Lu X, Zhu L , Zhang X, Yang N, Zhu Z, Liu Q
Received 26 May 2025
Accepted for publication 10 November 2025
Published 27 November 2025 Volume 2025:17 Pages 4891—4903
DOI https://doi.org/10.2147/IJWH.S533676
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Elie Al-Chaer
Xiaojuan Lu,* Lixia Zhu,* Xuegang Zhang, Ning Yang, Zhiwei Zhu, Qin Liu
Department of Gynecology, Kunshan First People’s Hospital, Kunshan, Jiangsu, 215300, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qin Liu, Department of Gynecology, Kunshan First People’s Hospital, No. 5 Qingyang Middle Road, Kunshan, Jiangsu, 215300, People’s Republic of China, Tel +86-512-57027134, Email liuqinchina@163.com
Purpose: Gene signatures offer superior power on prognosis and survival of patients over traditional single-gene biomarker. This study aimed to develop a nomogram based on an immunogenic cell death (ICD) and endoplasmic reticulum (ER) stress–related gene signature to predict the overall survival (OS) of patients with ovarian cancer (OC).
Materials and Methods: ICD- and ER stress–related genes were identified from public datasets and previous publications. Candidate genes were screened through differential expression and survival analyses. A prognostic risk score was established using Cox and LASSO regression. The model’s predictive performance was evaluated by Kaplan–Meier and multivariate analyses, and a nomogram was constructed to estimate individual survival probabilities.
Results: A seven-gene signature related to ICD and ER stress was developed to generate a prognostic risk score. Patients with low risk scores had significantly longer OS compared to high-risk patients. The signature correlated with immune features and remained an independent prognostic factor for OS in multivariate analysis. The prediction performance was good, with AUC values of 0.61, 0.64, and 0.67 for 1-, 2-, and 3-year OS in the training set, and 0.60, 0.61, and 0.60 in the validation set. The calibration curve showed good consistency between predicted and actual results.
Conclusion: We established a clinically applicable nomogram integrating a seven-gene ICD/ER stress signature to provide individualized survival prediction for OC patients. This tool may assist clinicians in risk stratification and personalized treatment planning.
Keywords: ovarian cancer, immunogenic cell death, endoplasmic reticulum stress, prognostic model
Department of Gynecology, Kunshan First People’s Hospital, Kunshan, Jiangsu, 215300, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Qin Liu, Department of Gynecology, Kunshan First People’s Hospital, No. 5 Qingyang Middle Road, Kunshan, Jiangsu, 215300, People’s Republic of China, Tel +86-512-57027134, Email liuqinchina@163.com
Purpose: Gene signatures offer superior power on prognosis and survival of patients over traditional single-gene biomarker. This study aimed to develop a nomogram based on an immunogenic cell death (ICD) and endoplasmic reticulum (ER) stress–related gene signature to predict the overall survival (OS) of patients with ovarian cancer (OC).
Materials and Methods: ICD- and ER stress–related genes were identified from public datasets and previous publications. Candidate genes were screened through differential expression and survival analyses. A prognostic risk score was established using Cox and LASSO regression. The model’s predictive performance was evaluated by Kaplan–Meier and multivariate analyses, and a nomogram was constructed to estimate individual survival probabilities.
Results: A seven-gene signature related to ICD and ER stress was developed to generate a prognostic risk score. Patients with low risk scores had significantly longer OS compared to high-risk patients. The signature correlated with immune features and remained an independent prognostic factor for OS in multivariate analysis. The prediction performance was good, with AUC values of 0.61, 0.64, and 0.67 for 1-, 2-, and 3-year OS in the training set, and 0.60, 0.61, and 0.60 in the validation set. The calibration curve showed good consistency between predicted and actual results.
Conclusion: We established a clinically applicable nomogram integrating a seven-gene ICD/ER stress signature to provide individualized survival prediction for OC patients. This tool may assist clinicians in risk stratification and personalized treatment planning.
Keywords: ovarian cancer, immunogenic cell death, endoplasmic reticulum stress, prognostic model