Clinical trial on prognosis prediction of ovarian cancer patients based on tumor proliferation and immune-related biomarkers
10.13699/j.cnki.1001-6821.2024.02.009
- VernacularTitle:基于肿瘤增殖和免疫相关生物标志物的卵巢癌患者预后预测的临床研究
- Author:
Yi-Long LIU
1
;
Xia HE
;
Xue-Wu SONG
;
Rong-Sheng TONG
Author Information
1. 电子科技大学医学院,四川成都 610054;乐山市人民医院药学部,四川乐山 614000;四川省医学科学院·四川省人民医院/电子科技大学附属医院药学部个体化药物治疗四川省重点实验室,四川成都 610072
- Keywords:
proliferation;
immunity;
ovarian cancer;
nomogram;
prognosis
- From:
The Chinese Journal of Clinical Pharmacology
2024;40(2):195-199
- CountryChina
- Language:Chinese
-
Abstract:
Objective To integrate tumor proliferation and immune-related biomarkers to construct a nomogram prediction model for predicting the prognosis of ovarian cancer patients.Methods We collected clinical information from patients diagnosed with epithelial ovarian cancer(EOC)between 2009 and 2013.Immunohistochemical staining was performed to detect the expression levels of KI67,epidermal growth factor receptor(EGFR)and programmed death-ligand 1(PD-Ll)in tumor tissues.We employed Lasso-Cox regression to identify variables and construct the nomogram model.We used time-dependent receiver operating characteristic(ROC)curves,concordance index,calibration curves,and decision curve analysis(DC A)curves to assess the model's discrimination,calibration,and net clinical benefit ability,respectively.Additionally,we conducted Kaplan-Meier survival analysis to assess the prognostic value of the model's risk score.Results We included a total of 131 EOC patients who were randomly assigned to the training set(n=79)and validation set(n=52)in a 6∶4 ratio.Lasso-Cox regression identified seven variables for constructing the nomogram prediction model.The AUCs for 1-,4-,and 6-year overall survival in the training set were 0.911,0.943,and 0.968,respectively,with a consistency index of 0.86[95％confidence interval(CI):0.81-0.91].In the validation set,the AUCs for 1-,4-,and 6-year overall survival were 0.830,0.797,and 0.828,respectively,with a consistency index of 0.71(95％CI:0.64-0.78).The calibration curves in both training and validation sets demonstrated strong agreement between model-predicted survival and actual outcomes(all P＞0.05).DCA curves indicated that the modeled net clinical benefit outperformed TNM staging.Patients with high-risk scores in the model exhibit poorer overall survival(P＜0.01)and progression-free survival(P＜0.01).Conclusion The successful development and validation of a nomogram prediction model based on tumor proliferation and immune-related biomarkers offer an efficient and straightforward clinical tool.This tool holds promise for enabling personalized treatment strategies for patients with ovarian cancer.
