The Mitochondrial Signature for Predicting Outcome of Early-Stage Breast Cancer by Machine Learning
Li, Yanni LU ; Sundquist, Kristina LU ; Wang, Xiao LU ; Sundquist, Jan LU and Memon, Ashfaque A LU
(2025)
In Clinical Breast Cancer
- Abstract
INTRODUCTION: Breast cancer, a leading female cancer worldwide, can be influenced by mitochondrial dysfunction. Dysregulation of mitochondria by the nuclear genome may cause breast cancer initiation and progression. However, the comprehensive investigation of mitochondrial-related genes as prognostic marker for the overall survival of early-stage breast cancer patients is still limited.
METHODS: To address this, we employed machine learning methods to identify a concise set of mitochondrial-related genes with high accuracy and reliability in predicting survival outcomes. Bulk transcriptome collected from Sweden Cancerome Analysis Network - Breast (SCANB) was divided into training and testing datasets and the Cancer Genome Atlas... (More)
INTRODUCTION: Breast cancer, a leading female cancer worldwide, can be influenced by mitochondrial dysfunction. Dysregulation of mitochondria by the nuclear genome may cause breast cancer initiation and progression. However, the comprehensive investigation of mitochondrial-related genes as prognostic marker for the overall survival of early-stage breast cancer patients is still limited.
METHODS: To address this, we employed machine learning methods to identify a concise set of mitochondrial-related genes with high accuracy and reliability in predicting survival outcomes. Bulk transcriptome collected from Sweden Cancerome Analysis Network - Breast (SCANB) was divided into training and testing datasets and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) was included as the external validation cohort. The 1136 known mitochondrial-related genes were analysed using univariate Cox regression, bootstrap and Lasso Cox regression in the SCANB training cohort for model construction.
RESULTS: We identified a 14-gene mitochondrial signature that independently predicts the survival outcome of breast cancer (adjusted hazard ratio [HR]: 2.08, 95% confidence interval [CI]: 1.20-3.62) in the SCANB dataset. A highly predictive nomogram was further constructed by integrating the mitochondrial signature with clinical variables, enabling robust prediction of overall survival at 1-, 3- and 5-year. This model demonstrated strong predictive capability in both the training cohort (the area under the receiver operating characteristic [ROC] curve [AUC]: 0.84, 0.79, 0.78) and validation cohort (AUC: 0.92, 0.83, 0.78).
CONCLUSION: In this study, we suggested a novel mitochondrial signature model by comprehensively analysing mitochondrial-related genes, which have the potential to accurately predict the clinical prognosis at the early stages of breast cancer.
(Less)
- author
- Li, Yanni
LU
; Sundquist, Kristina
LU
; Wang, Xiao
LU
; Sundquist, Jan
LU
and Memon, Ashfaque A
LU
- organization
- publishing date
- 2025-05-03
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Clinical Breast Cancer
- publisher
- Elsevier
- external identifiers
-
- scopus:105005946645
- pmid:40414762
- ISSN
- 1526-8209
- DOI
- 10.1016/j.clbc.2025.04.020
- language
- English
- LU publication?
- yes
- additional info
- Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.
- id
- 766b4b7f-438e-45c2-873a-9a4fa1617eb5
- date added to LUP
- 2025-05-26 15:30:15
- date last changed
- 2025-07-12 04:35:38
@article{766b4b7f-438e-45c2-873a-9a4fa1617eb5,
abstract = {{<p>INTRODUCTION: Breast cancer, a leading female cancer worldwide, can be influenced by mitochondrial dysfunction. Dysregulation of mitochondria by the nuclear genome may cause breast cancer initiation and progression. However, the comprehensive investigation of mitochondrial-related genes as prognostic marker for the overall survival of early-stage breast cancer patients is still limited.</p><p>METHODS: To address this, we employed machine learning methods to identify a concise set of mitochondrial-related genes with high accuracy and reliability in predicting survival outcomes. Bulk transcriptome collected from Sweden Cancerome Analysis Network - Breast (SCANB) was divided into training and testing datasets and the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) was included as the external validation cohort. The 1136 known mitochondrial-related genes were analysed using univariate Cox regression, bootstrap and Lasso Cox regression in the SCANB training cohort for model construction.</p><p>RESULTS: We identified a 14-gene mitochondrial signature that independently predicts the survival outcome of breast cancer (adjusted hazard ratio [HR]: 2.08, 95% confidence interval [CI]: 1.20-3.62) in the SCANB dataset. A highly predictive nomogram was further constructed by integrating the mitochondrial signature with clinical variables, enabling robust prediction of overall survival at 1-, 3- and 5-year. This model demonstrated strong predictive capability in both the training cohort (the area under the receiver operating characteristic [ROC] curve [AUC]: 0.84, 0.79, 0.78) and validation cohort (AUC: 0.92, 0.83, 0.78).</p><p>CONCLUSION: In this study, we suggested a novel mitochondrial signature model by comprehensively analysing mitochondrial-related genes, which have the potential to accurately predict the clinical prognosis at the early stages of breast cancer.</p>}},
author = {{Li, Yanni and Sundquist, Kristina and Wang, Xiao and Sundquist, Jan and Memon, Ashfaque A}},
issn = {{1526-8209}},
language = {{eng}},
month = {{05}},
publisher = {{Elsevier}},
series = {{Clinical Breast Cancer}},
title = {{The Mitochondrial Signature for Predicting Outcome of Early-Stage Breast Cancer by Machine Learning}},
url = {{http://dx.doi.org/10.1016/j.clbc.2025.04.020}},
doi = {{10.1016/j.clbc.2025.04.020}},
year = {{2025}},
}