Clinical and Translational Research
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Oncol. Jan 24, 2025; 16(1): 94813
Published online Jan 24, 2025. doi: 10.5306/wjco.v16.i1.94813
Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response
Zhi-Jian Tang, Yuan-Ming Pan, Wei Li, Rui-Qiong Ma, Jian-Liu Wang
Zhi-Jian Tang, Rui-Qiong Ma, Jian-Liu Wang, Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing 100044, China
Yuan-Ming Pan, Wei Li, Cancer Research Center, Beijing Chest Hospital, Beijing 101149, China
Wei Li, Department of Thoracic Surgery, Sichuan Provincial People's Hospital, Chengdu 610072, Sichuan Province, China
Author contributions: Tang ZJ and Wang JL designed the research study; Tang ZJ, Wang JL and Pan YM performed the research; Li W and Ma RQ contributed to analytical tools; Tang ZJ, Li W and Wang JL analyzed the data and wrote the manuscript; All authors read and approved the final manuscript.
Supported by National Key Technology Research and Developmental Program of China, No. 2022YFC2704400 and No. 2022YFC2704405.
Conflict-of-interest statement: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: 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
Received: March 26, 2024
Revised: May 17, 2024
Accepted: June 5, 2024
Published online: January 24, 2025
Processing time: 218 Days and 5.5 Hours
Abstract
BACKGROUND

Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses.

AIM

To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.

METHODS

Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Mitochondrial genes were sourced from the MitoCarta3.0 database. The discovery cohort for model construction was created from 70% of the patients, whereas the remaining 30% constituted the validation cohort. Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm, the overall survival time and immunotherapy efficacy (complete or partial response) of patients were predicted.

RESULTS

In total, 375 patients with OC were included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve of the prognostic model was 0.7268 [95% confidence interval (CI): 0.7258-0.7278] in the discovery cohort and 0.6475 (95%CI: 0.6466-0.6484) in the validation cohort. The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444 (95%CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95%CI: 0.6667-1.0000) in the validation cohort.

CONCLUSION

The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC, providing valuable insights into personalized treatment strategies.

Keywords: Ovarian cancer; Mitochondria; Prognosis; Immunotherapy; Neural network

Core Tip: In this study, we found that mitochondrial genes and neural networks can be used to predict ovarian cancer prognosis and immunotherapy response. These models were evaluated in detail. The average area under the receiver operating characteristic curve of the prognostic model was 0.7268 and 0.6475 for the discovery and validation cohorts, respectively. The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444 and 0.9167 for the discovery and validation cohorts, respectively. The Hosmer-Lemeshow goodness of fit test showed that the model had a good calibration performance.