Tang ZJ, Pan YM, Li W, Ma RQ, Wang JL. Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response. World J Clin Oncol 2025; 16(1): 94813 [DOI: 10.5306/wjco.v16.i1.94813]
Corresponding Author of This Article
Jian-Liu Wang, MD, Professor, Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11 Xizhimen South Street, Beijing 100044, China. wangjianliu@pkuph.edu.cn
Research Domain of This Article
Medical Informatics
Article-Type of This Article
Clinical and Translational Research
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Clin Oncol. Jan 24, 2025; 16(1): 94813 Published online Jan 24, 2025. doi: 10.5306/wjco.v16.i1.94813
Table 1 Clinical features of patients in this study
Discovery cohort
Validation cohort
Statistical method
P value
Prognostic model
(n = 262)
(n = 113)
Survival time in days
Wilcoxon
0.5446
Median (IQR)
1012 (547, 1712)
1032 (394, 1562)
Alive
χ2 test
0.5337
Yes
104 (39.69)
41 (36.28)
No
158 (60.31)
72 (63.72)
Immunotherapy efficacy model
(n = 18)
(n = 8)
Sensitivity to immunotherapy
Fisher’s exact
0.3945
Yes
9 (50.00)
2 (25.00)
No
9 (50.00)
6 (75.00)
Table 2 Overall performance of the ovarian cancer prognostic model
Discovery cohort
Validation cohort
AUC
0.7268
0.6475
AUC 95%CI
0.7258-0.7278
0.6466-0.6484
Table 3 Performance of the ovarian cancer prognostic model when truncated at 1 year, 2 years, and 3 years
Discovery cohort
Validation cohort
1 year
2 years
3 years
1 year
2 years
3 years
AUC
0.7597
0.7734
0.7461
0.6827
0.725
0.7087
AUC 95%CI
0.6252-0.8942
0.6908-0.8560
0.6789-0.8134
0.5114-0.8541
0.6128-0.8373
0.6126-0.8049
Specificity
0.7869
0.9481
0.9022
0.7451
0.7931
0.7895
Sensitivity
0.6667
0.58
0.5897
0.6364
0.6538
0.6216
Accuracy
0.7786
0.8779
0.8092
0.7345
0.7611
0.7345
NPV
0.9697
0.9054
0.8384
0.95
0.8846
0.8108
PPV
0.1875
0.725
0.7188
0.2121
0.4857
0.5897
P value, goodness of fit
0.6699
0.6304
0.4325
0.0826
0.1042
0.1084
Table 4 Overall performance of the ovarian cancer immunotherapy efficacy model
Discovery cohort
Validation cohort
AUC
0.9444
0.9167
AUC 95%CI
0.8333-1.0000
0.6667-1.0000
Specificity
1.0000
0.8333
Sensitivity
0.8889
1.0000
Accuracy
0.9444
0.8750
NPV
0.9000
1.0000
PPV
1.0000
0.6667
P value, goodness of fit
0.3708
0.1175
Citation: Tang ZJ, Pan YM, Li W, Ma RQ, Wang JL. Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response. World J Clin Oncol 2025; 16(1): 94813